Professor Pei Xiao

Rate Splitting Multiple Access (RSMA) precoder design with the practical finite-alphabet constellations instead of Gaussian inputs has been addressed in this paper. Considering a multiuser (MU) multiple-input single-output (MISO) broadcast channel (BC) system, we derive a generalized expression of the achievable rate for each user, in a way that the derived expression is generically applicable, e.g., for both underloaded and overloaded cases. Building upon the achievable rate expression, we formulate a multi-objective problem that maximizes the weighted sum rate (WSR) of the considered system, which incorporates with the optimization of the RS precoder for both common and private symbol streams in RSMA. The emphasis here is that our derivation of the achievable rate expression, the problem formulation of the WSR and the optimization of the RSMA precoder all involve the finite alphabet constellation constraint. An iterative gradient descent algorithm with alternative optimization and line search methods is applied to solve the optimization problem. Numerical results show that RSMA can reach the maximum achievable WSR, under both underloaded and overloaded scenarios, with less transmit power compared to the traditional schemes, e.g., space division multiple access (SDMA) and power-domain non-orthogonal multiple access (NOMA). Moreover, thanks to its flexibility, RSMA subsumes both SDMA and NOMA as its subset to fit into different scenarios such as underloaded and overloaded cases with different constellation sizes.

In this paper, we propose a fluid antenna (FA) enabled joint transmit and receive index modulation (FA-JTRIM) transmission mechanism for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems. By integrating the methodologies of FA and IM, the proposed scheme achieves enhanced spectral efficiency (SE) while requiring only a single radio frequency (RF) chain at both the transmitter and receiver. The proposed scheme offers a low hardware cost and power consumption transmission mechanism for the RIS-aided mmWave communication systems. Specifically, the encoding of information bits encompasses not only the modulated symbol but also the indices of transmit FA positions and receive antennas. To achieve a reliability-complexity trade-off, two types of detectors are introduced for the proposed FA-JTR-IM scheme, including the optimal maximum likelihood (ML) detector and two-step sequential (TSS) detector. Based on the ML detector, we derive the expression for the conditional pair-wise error probability of the proposed FA-JTR-IM scheme. Additionally, we provide the closed-form expressions for the unconditional PEP under the finite-path and infinite-path channel conditions, respectively. Simulation results demonstrate the superiority of the proposed FA-JTR-IM scheme in terms of error performance over its conventional benchmark schemes under the same SE condition

—The Internet of Things (IoT) introduces diverse requirements and ubiquitous connections, necessitating efficient and affordable energy consumption as the ecosystem continues to grow. To address this challenge, we investigate a pure nonorthogonal multiple access (pure-NOMA) beamforming scheme to enhance system capacity by accommodating more IoT devices within the same spectrum. An energy efficiency (EE) maximization problem is formulated, jointly optimizing the beamforming matrix, power allocation, and device clustering. Due to thedynamic nature of the transmission channel and the coupling non-convex mixed integer nonlinear programming problem, it is challenging to solve this problem by conventional mathematical methods. Additionally, the high dimensionality and coupling non-convex mixed integer nonlinear programming problem pose significant challenges for traditional reinforcement learning (RL) methods. To overcome these issues, we propose a curiositydriven approach that leverages intrinsic information from the base station (BS) to achieve energy efficient resource allocation. Simulation results demonstrate that pure-NOMA offers up to a 25% improvement in EE compared to hybrid-NOMA, while the curiosity-driven learning method outperforms baseline techniques—including deep reinforcement learning (DRL), zeroforcing, and random methods—achieving a 14.78% reward gain over the DRL approach. The effectiveness of the proposed method is validated across various beam settings, device counts, qualityof service requirements, and time consumption metrics, all while maintaining comparable computational complexity.

This paper provides the mobility and coverage evaluation of New Radio (NR) Physical Downlink Control Channel (PDCCH) for Point-to-Multipoint (PTM) use cases, e.g., eMBMS (evolved Multimedia Broadcast Multicast Services). The evaluation methodology is based on analyses and link level simulations where the channel model includes AWGN, TDL-A, TDL-C as well as a modified 0dB echo to model different PTM scenarios. The final version of this work aims to provide insightful guidelines on the delay/echo tolerance of the NR PDCCH in terms of mobility and coverage. In this paper, it is observed that under eMBMS scenario, i.e. SFN channel, due to the time domain granularity of pilots distributed inside the PDCCH region, the system can support very high user movement speed/Doppler with an relatively low requirement on the transmit Signal/Carrier-to-Noise Ratio (SNR/CNR). On the other hand however, the system falls short on its coverage due to the low frequency domain granularity of pilots that effects the channel estimation accuracy.

5G New Radio (NR) Release 15 has been specified in June 2018. It introduces numerous changes and potential improvements for physical layer data transmissions, although only point-to-point (PTP) communications are considered. In order to use physical data channels such as the Physical Downlink Shared Channel (PDSCH), it is essential to guarantee a successful transmission of control information via the Physical Downlink Control Channel (PDCCH). Taking into account these two aspects, in this paper, we first analyze the PDCCH processing chain in NR PTP as well as in the state-of-the-art Long Term Evolution (LTE) point-to-multipoint (PTM) solution, i.e., evolved Multimedia Broadcast Multicast Service (eMBMS). Then, via link level simulations, we compare the performance of the two technologies, observing the Bit/Block Error Rate (BER/BLER) for various scenarios. The objective is to identify the performance gap brought by physical layer changes in NR PDCCH as well as provide insightful guidelines on the control channel configuration towards NR PTM scenarios.

In this work, we provide the first attempt to evaluate error performance of Rate-Splitting (RS) based transmission strategies with constellation-constrained coding/modulation. The considered scenario is an overloaded multigroup multicast, where RS can mitigate the inter-group interference thus achieve a better max-min fair group rate over conventional transmission strategies. We bridge the RS-based rate optimization with modulation-coding scheme selection, and implement them in a developed transceiver framework with either linear or non-linear receiver, where the latter equips with a generalized sphere decoder. Simulation results of a coded bit error rate demonstrate that, while the conventional strategies suffer from the error floor in the considered scenario, the RS-based strategy delivers a superior performance even with low complexity receiver techniques. The proposed analysis, transceiver framework and evaluation methodology provide a generic baseline solution to validate the effectiveness of the RS-based system design in practice.

A cell-free massive multiple-input multiple-output (MIMO) uplink is investigated in this paper. We address a power allocation design problem that considers two conflicting metrics, namely the sum rate and fairness. Different weights are allocated to the sum rate and fairness of the system, based on the requirements of the mobile operator. The knowledge of the channel statistics is exploited to optimize power allocation. We propose to employ large scale-fading (LSF) coefficients as the input of a twin delayed deep deterministic policy gradient (TD3). This enables us to solve the non-convex sum rate fairness trade-off optimization problem efficiently. Then, we exploit a use-and-then-forget (UatF) technique, which provides a closed-form expression for the achievable rate. The sum rate fairness trade-off optimization problem is subsequently solved through a sequential convex approximation (SCA) technique. Numerical results demonstrate that the proposed algorithms outperform conventional power control algorithms in terms of both the sum rate and minimum user rate. Furthermore, the TD3-based approach can increase the median of sum rate by 16%-46% and the median of minimum user rate by 11%-60% compared to the proposed SCA-based technique. Finally, we investigate the complexity and convergence of the proposed scheme.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access scheme for enabling massive connectivity in next generation wireless networks. However, current SCMA codebooks are designed with the same size, leading to inflexibility of user grouping and supporting diverse data rates. To address this issue, we propose a variable modulation SCMA (VMSCMA) that allows users to employ codebooks with different modulation orders. To guide the VM-SCMA design, a VM matrix (VMM) that assigns modulation orders based on the SCMA factor graph is first introduced. We formulate the VM-SCMA design using the proposed average inverse product distance and the asymptotic upper bound of sum-rate, and jointly optimize the VMM, VM codebooks, power and codebook allocations. The proposed VM-SCMA not only enables diverse date rates but also supports different modulation order combinations for each rate. Leveraging these distinct advantages, we further propose an adaptive VM-SCMA (AVM-SCMA) scheme which adaptively selects the rate and the corresponding VM codebooks to adapt to the users’ channel conditions by maximizing the proposed effective throughput. Simulation results show that the overall designs are able to simultaneously achieve a high-level system flexibility, enhanced error rate results, and significantly improved throughput performance, when compared to conventional SCMA schemes.

—In the Industrial Internet of Things (IIoT), outdoor electronic devices serve crucial roles across sectors, providing vital data for decision-making. However, their exposure to open outdoor environments makes them vulnerable to unauthorized access, physical theft, or compromise, endangering both the device and its data. Ensuring the security of outdoor devices and their data is thus critical. This study addresses data security in outdoor IIoT devices by supporting the encryption of all IIoT-related data in device memory. Accessing and retrieving this data requires operations on encrypted data. Hence, we introduce a Searchable Symmetric Encryption (SSE) scheme called MI3SE, which ensures each device's encryption key is unique and valid for a period based on the device's security sensitivity. Moreover, MI3SE meets key security requirements, including confidentiality, integrity, forward secrecy, and backward secrecy. It is specifically designed to mitigate physical compromise and query pattern analysis through a two-keyword query approach and withstand various attacks, as validated by rigorous security analysis. Comparative evaluations against benchmark schemes underscore the efficacy of MI3SE in terms of both security and performance. Moreover, comprehensive non-mathematical security analysis and simulation experiments affirm the enhanced accuracy and efficacy of MI3SE in securing sensitive data stored in outdoor IIoT devices.

In this paper, we investigate a novel integrated communications, computing, and sensing (ICCS) network, where a multi-functional base station (BS) performs downlink communication , target sensing and edge computing. To examine the overall performance of the system under consideration, a weighted sum rate (WSR) maximization problem is formulated by jointly optimizing information beamforming, sensing covariance matrix, sensing echo receiving beamforming, offloading signal receiving beamforming, local computing resources, edge computing resources and offloading strategy. The formulated problem consists of multiple coupled variables, which leads to its non-convexity. To circumvent this issue, we propose an efficient alternating iterative optimization algorithm, which decomposes the original problem into four subproblems to be solved alternately. Specifically, we develop an effective algorithm based on the quadratic transform fractional programming approach to optimize the information beamforming, sensing covariance matrix, sensing echo receiving beamforming and edge computing resources. Then, the La-grangian dual method is used to optimize the offloading signal receiving beamforming. We derive a closed-form solution for the local computing resource. Meanwhile, a coordinate descent (CD) method is proposed to obtain the offloading strategy. The final solutions are obtained by alternatively optimizing the above subproblems until convergence. Finally, numerical results are presented to show the efficiency of the proposed algorithm in comparison to the existing benchmark schemes. Index Terms—Integrated sensing and communication, computing , weighted sum rate.

This paper studies the codebook design for sparse code multiple access (SCMA) based visible light communication (VLC) impaired by shot noise. By focusing on the typical Rician fading channels, we first derive a lower bound of the mutual information of VLC with shot noise and present a design metric called minimum normalized Euclidean distance (MNED). We then propose a novel codebook design approach for VLC including novel non-linear compensation (NLC) constellation and power scheduling matrix under the non-negative and real constraint. Simulation results demonstrate that our proposed codebook design leads to a higher MNED and thus significantly improved bit error performance over the existing SCMA codebooks for VLC systems.

—This paper develops a cell-free massive multiple-input multiple-output enabled multi-access edge computing (MEC) system consisting of multiple users and one central processing unit (CPU) equipped with multiple access points (APs). It aims to achieve seamless task offloading and computation. By minimizing the average energy consumption, the joint optimization of AP clustering, task splitting coefficient, transmit power and computation resource of users is formulated as a mixed integer nonlinear programming problem. The formulated problem is complicated non-convex due to the coupled discrete and continuous variables, resulting in high complexity and non-real-time to directly obtain the optimal solution. To tackle this problem, we propose a proximal policy optimization (PPO)-based hierarchical deep reinforcement learning (HDRL) algorithm , where the discrete and continuous variables are iteratively solved by the designed PPO-based high-level and low-level agents. Simulation results demonstrate the superiority of the proposed algorithm in terms of average energy consumption.

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity.

This paper proposes a secure transmission in reconfigurable intelligent surfaces (RIS) aided non-terrestrial cooperative networks (NTCN), where the practical phase-dependent model is considered in which the RIS reflection amplitudes change with the corresponding discrete phase shifts. Moreover, we employ a full-duplex transmission scheme at the relay nodes to reduce the long-range signal loss and improve the security between the satellite and the relay node. To solve the complex non-convex optimization problem of the joint RIS reflection coefficient and relay selection optimization, we propose the deep cascade correlation learning (DCCL) algorithm to enhance optimization efficiency. Simulation results show that the proposed DCCL-based method significantly improves the secrecy capacity compared to the random relay selection and RIS coefficient methods.

AbstractA novel high-isolation, monostatic, circularly polarized (CP) simultaneous transmit and receive (STAR) anisotropic dielectric resonator antenna (DRA) is presented. The Proposed antenna is composed of two identical but orthogonally positioned annular sectoral anisotropic dielectric resonators. Each circularly polarized (CP) resonator consists of alternating stacked dielectric layers of relative permittivities of 2 and 15 and is excited by a coaxial probe from the two opposite ends to have left and right-hand CP. Proper element spacing and a square absorber are placed between the resonators to maximize Tx/Rx isolation. Such a structure provides an in-band full-duplex (IBFD) CP-DRA system. Measurement results exhibit high Tx/Rx isolation better than 50 dB over the desired operating bandwidth (5.87–5.97 GHz) with a peak gain of 5.49 and 5.08 dBic for Ports 1 and 2, respectively.

High mobility scenarios may be typical for different applications such as low earth orbit (LEO) satellite and vehicle-to-everything (V2X) communications. A standardized approach to dealing with high mobility scenarios is using flexible sub-frame structures including a higher pilot density in the time domain, which leads to reduced spectrum efficiency. We propose a supplementary algorithm to improve multiple antenna receiver performance in high mobility scenarios for the given sub-frame structure compared to the conventional 3GPP pilot and data based interference rejection receivers. The main feature of high mobility (non-stationary) scenarios is that different symbols in the desired signal sub-frame may be received under different propagation and/or interference conditions. Recently, we have addressed a non-stationary interference rejection scenario in slowly varying propagation environment with asynchronous (intermittent) interference by means of developing an interference rejection combining algorithm, where the pilot based estimate of the interference plus noise covariance matrix is regularized by the data based estimate of the covariance matrix. In this paper, we: 1) extend the data regularized solution to the general high mobility scenarios, and 2) demonstrate its efficiency compared to the conventional pilot and data based receivers for different sub-frame formats in the uplink transmissions in the LEO satellite scenario with high residual Doppler frequency with and without hardware impairments.

Decentralized joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment is considered. An important feature of the considered environment is that altering the transmit beamforming pattern at some node generally creates more significant changes to interference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence behavior of the proposed algorithm compared to the best response and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.

In this paper, a novel intelligent transmission surface (ITS) assisted terahertz (THz) wideband massive multiple-input multiple-output (MIMO) non-terrestrial communication architecture is conceived, which is capable of reducing the hardware cost and power consumption remarkably compared to traditional architectures. To further address the beam squint impact in the THz wideband system, an angle-based hybrid beamformer is designed for the proposed architecture, which can effectively suppress the beam squint and maintain high spectral efficiency (SE) performance. Numerical results demonstrate that the proposed scheme is capable of approaching the optimal full-digital architecture in terms of the SE performance, and the proposed method can achieve significant energy efficiency performance gains over other existing architectures.

Non-terrestrial networks (NTNs) are expected to play a pivotal role in the future wireless ecosystem. Due to its high-dynamic characteristics, the accurate estimation and compensation of carrier frequency offset (CFO) are crucial for supporting 5G new radio (NR) enabled satellite direct access. With emphasis on ensuring reliable uplink synchronization, we propose a clustering-neural network based CFO estimation scheme by virtue of NR random access preambles. By leveraging the sparsity and regularity of input samples, the proposed scheme can achieve fast and precise prediction of CFOs, while establishing robustness against time uncertainty and channel variation within a satellite beam. Simulation results validate the feasibility of our scheme in various NTN scenarios, and demonstrate its superiority in terms of stable estimation performance over the existing schemes.

In MIMO multi-user networks, inter-user interference (IUI) significantly affects the system performance. To handle this problem, this paper proposes the reconfigurable intelligent surface assisted cooperative interference alignment scheme (RIS-CIA). The core idea of this work is that the base station and full-duplex users jointly design space-time precoding matrices, which can reduce the dimension of the interference space on the user side. Besides, the additional interference caused by the information exchange process is split into sub-blocks by space-time precoding, then eliminated by interference nulling assisting by the passive RIS. The simulation results show that the RIS-CIA scheme with few numbers of elements obtains higher DoF than that of benchmark schemes with a huge number of elements.

Terahertz (THz) and intelligent reflecting surface (IRS) have been regarded as two promising technologies to improve the capacity and coverage for future 6G networks. Generally, IRS is usually equipped with large-scale elements when implemented at THz frequency. In this case, the near-field model and beam squint should be considered. Therefore, in this paper, we investigate the far-field and near-field beam squint problems in THz IRS communications for the first time. The far-field and near-field channel models are constructed based on the different electromagnetic radiation characteristics. Next, we first analyze the far-field beam squint and its effect for the beam gain based on the cascaded base station (BS)-IRS-user channel model, and then the near-field case is studied. To overcome the far-field and near-field beam squint effects, we propose to apply delay adjustable metasurface (DAM) to IRS, and develop a scheme of optimizing the reflecting phase shifts and time delays of IRS elements, which effectively eliminates the beam gain loss caused by beam squint. Finally, simulations are conducted to demonstrate the effectiveness of our proposed schemes in combating the near and far field beam squint.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least $97\%$ compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at \url{https://github.com/ethanlq/SCMA-codebook}.

Non-orthogonal multiple access schemes (NOMA), such as sparse code multiple access (SCMA), are among the most promising technologies to support massive numbers of connected devices. Still, to minimize the transmission delay and to maximize the utilization of the transmission channel, "grant-free" NOMA techniques are required that eliminate any prior information exchange between the users and the base-stations. However, if a large number of users transmit simultaneously in an "unsupervised" manner, (i.e., without any prior signaling for controlling the number of users and the corresponding transmission patterns), it is likely that a large number of users may share the same frequency-resource element, rendering the corresponding user detection impractical. In this context, we present a new multi-user detection approach, which aims to maximize the detection performance, with respect to given processing and latency limitations. We show that our approach enables practical detection for grant-free SCMA schemes that support hundreds of interfering users, with a complexity that is up to two orders of magnitude less than that of conventional detection approaches.

With the popularity of high-mobility scenarios in future networks, the issue of security and reliability of their communications is a growing concern. In this paper, a secure transmission scheme for affine frequency division multiplexing(AFDM) systems is proposed, which guides the AFDM modulation parameter design with the information obtained from integrated sensing function and ultimately obtains a differentiated demodulation performance. In order to realize the sensing function based on AFDM, we first establish the input-output relationship of the vectorization of the AFDM system, and propose a radar target echo channel estimation scheme to estimate the channel. The base station sends a set of non-orthgonal multiple access (NOMA) signals with pilot superposition information to the user, and receives radar targets in the discrete affine Fourier transform (DAFT) domain. It enables basestation (BS) to sense the channel delay, doppler shift and channel gain on a fast time axis, make base station and the receiving end have the same modulation and demodulation parameters and maintain good sensing performance even in large Doppler shift scenarios. The base station and the legitimate users have the same confidentiality parameters, effectively preventing eavesdroppers from stealing information. The simulation results verify the effectiveness of our proposed AFDM-based system in high-mobility scenarios and the security of information transmission.

Orthogonal time-frequency space (OTFS), which exhibits beneficial advantages in high-mobility scenarios, has been considered as a promising technology in future wireless communication systems. In this paper, a universal model for OTFS systems with generalized waveform has been developed. Furthermore, the average bit error probability (ABEP) upper bounds of the optimal maximum likelihood (ML) detector are first derived for OTFS systems with generalized wave-forms. Specifically, for OTFS systems with the ideal waveform, we elicit the ABEP bound by recombin-ing the transmitted signal and the received signal. For OTFS systems with practical waveforms, a universal ABEP upper bound expression is derived using moment generating-function (MGF), which is further extended to MIMO-OTFS systems. Numerical results validate that our theoretical ABEP upper bounds are concur with the simulation performance achieved by ML detectors.

—This paper focuses on the low-complexity multiuser detection of coded low-density signature (LDS) systems where the numbers of resources and users are both large. Typically, the detection complexity using the conventional message passing algorithm grows exponentially with the number of users occupied at each resource, making it unaffordable for large-scale LDS systems. To address this problem, we propose to apply orthogonal approximate message passing (OAMP) to detect LDS symbols with polynomial complexity. The numerical results demonstrate the superiority of the proposed method in terms of the error performance over the traditional turbo receiver.

Cell-free massive multiple-input multiple-output (CF-mMIMO) has been considered as one of the potential technologies for beyond-5G and 6G to meet the demand for higher data capacity and uniform service rate for user equipment. However, reusing the same pilot signals by several users, owing to limited pilot resources, can result in the so-called pilot contamination problem, which can prevent CF-mMIMO from unlocking its full performance potential. It is challenging to employ classical pilot assignment (PA) methods to serve many users simultaneously with low complexity; therefore, a scalable and distributed PA scheme is required. In this paper, we utilize a learning-based approach to handle the pilot contamination problem by formulating PA as a multi-agent static game, developing a two-level hierarchical learning algorithm to mitigate the effects of pilot contamination, and presenting an efficient yet scalable PA strategy. We first model a PA problem as a static multi-agent game with P teams (agents), in which each team is represented by a specific pilot. We then define a multi-agent structure that can automatically determine the most appropriate PA policy in a distributed manner. The numerical results demonstrate that the proposed PA algorithm outperforms previous suboptimal algorithms in terms of the per-user spectral efficiency (SE). In particular, the proposed approach can increase the average SE and 95%-likely SE by approximately 2.2% and 3.3%, respectively, compared to the best state-of-the-art solution.

—This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes. Index Terms—Intelligent reflecting surface (IRS), wireless powered mobile edge computing and caching (WP-MECC), utility

In this paper, average bit error probability (ABEP) bound of optimal maximum likelihood (ML) detector is first derived for ultra massive (UM) multiple-input-multiple-output (MIMO) system with generalized amplitude phase modulation (APM), which is confirmed by simulation results. Furthermore , a minimum residual criterion (MRC) based low-complexity near-optimal ML detector is proposed for UM-MIMO system. Specifically, we first obtain an initial estimated signal by a conventional detector, i.e., matched filter (MF), or minimum mean square error (MMSE) and so on. Furthermore, MRC based error correction mechanism (ECM) is proposed to correct the erroneous symbol encountered in the initial result. Simulation results are shown that the performance of the proposed MRC-ECM based detector is capable of approaching theoretical ABEP of ML, despite only imposing a slightly higher complexity than that of the initial detector.

The satellite-terrestrial downlink transmissions with larger coverage areas face more severe security challenges than terrestrial cellular communications due to the limitation of the satellite's power and the complexity of the channel. In this paper, the system model consists of one satellite, one legitimate user and multiple randomly distributed eavesdroppers while taking into account practical hardware impairments (HIs) at all transceivers involved. The Shadowed-Rician fading model is adopted since it is a perfect fit for the satellite-terrestrial channel. Besides, the full-duplex (FD) operation is introduced in the destination where the receiver acquires the information signal and simultaneously emits the artificial noise (AN) to interfere with the nearby eavesdroppers. The maximal ratio combination scheme is applied to the destination to improve security. To facilitate evaluation of the effect of various parameters on the security performance, the closed-form expressions of secrecy outage probability and ergodic secrecy capacity for the system are derived under the stochastic geometry framework. Finally, the simulation results show that the AN plays a pivotal role in improving the security performance, while the HIs can greatly deteriorate the performance, especially in the high transmit power regime. In addition, compared with the traditional half-duplex (HD) receiver, the FD receiver have better security performance and can significantly alleviate the performance deterioration caused by HIs.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology in wireless communications. Simultaneously transmitting and reflecting RIS (STAR-RISs) in particular have garnered significant attention due to their dual capabilities of simultaneous transmission and reflection, underscoring their potential applications in critical scenarios within the forthcoming sixth-generation (6G) technology landscape. Moreover, full-duplex (FD) systems have emerged as a breakthrough research direction in wireless transmission technology due to their high spectral efficiency. This paper explores the application potential of STAR-RIS in FD systems for future wireless communications, presenting an innovative technology that provides robust self-interference cancellation (SIC) capabilities for FD systems. We utilize the refraction functionality of STAR-RIS enhances the transmission capacity of FD systems, while its reflection functionality is used to eliminate self interference within the FD system. We delve into the applications of two different types of STAR-RIS in FD systems and compare their performance through simulations. Furthermore, we discuss the performance differences of STAR-RIS empowered FD systems under various configurations in a case study, and demonstrate the superiority of the proposed deep learning-based optimization algorithm. Finally, we discuss possible future research directions for STAR-RIS empowered FD systems.

Satellite communication system is expected to play a vital role for realizing various remote internet of things (IoT) applications in 6G vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this paper, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

A cell-free Massive multiple-input multiple-output (MIMO) uplink is considered, where the access points (APs) are connected to a central processing unit (CPU) through limited-capacity wireless microwave links. The quantized version of the weighted signals are available at the CPU, by exploiting the Bussgang decomposition to model the effect of quantization. A closed-form expression for spectral efficiency is derived taking into account the effects of channel estimation error and quantization distortion. The energy efficiency maximization problem is considered with per-user power, backhaul capacity and throughput requirement constraints. To solve this non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design, and power allocation. The receiver filter coefficient design is formulated as a generalized eigenvalue problem whereas a successive convex approximation (SCA) and a heuristic sub-optimal scheme are exploited to convert the power allocation problem into a standard geometric programming (GP) problem. An iterative algorithm is proposed to alternately solve each sub-problem. Complexity analysis and convergence of the proposed schemes are investigated. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation.

Network densification with small cell deployment is being considered as one of the dominant themes in the fifth generation (5G) cellular system. Despite the capacity gains, such deployment scenarios raise several challenges from mobility management perspective. The small cell size, which implies a small cell residence time, will increase the handover (HO) rate dramatically. Consequently, the HO latency will become a critical consideration in the 5G era. The latter requires an intelligent, fast and light-weight HO procedure with minimal signalling overhead. In this direction, we propose a memory-full context-aware HO scheme with mobility prediction to achieve the aforementioned objectives. We consider a dual connectivity radio access network architecture with logical separation between control and data planes because it offers relaxed constraints in implementing the predictive approaches. The proposed scheme predicts future HO events along with the expected HO time by combining radio frequency performance to physical proximity along with the user context in terms of speed, direction and HO history. To minimise the processing and the storage requirements whilst improving the prediction performance, a user-specific prediction triggering threshold is proposed. The prediction outcome is utilised to perform advance HO signalling whilst suspending the periodic transmission of measurement reports. Analytical and simulation results show that the proposed scheme provides promising gains over the conventional approach.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs on the same hardware platform, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via the reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system in the mmWave band. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the radar desired waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is applied to determine the communication beamforming vector and the IRS phase shift. Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.

This paper studies an intelligent reflecting surface (IRS)-empowered wireless powered communication network (WPCN) in Internet of Things (IoT) networks. In particular, a power station (PS) with multiple antennas uses energy beamforming to enable wireless charging to multiple IoT devices, in the downlink wireless energy transfer (WET) phase; then, during the uplink wireless information transfer (WIT) phase, these IoT devices utilise the harvested energy to concurrently transmit their individual information signal to a multi-antenna access point (AP), which equips with multi-user decomposition (MUD) techniques to reconstruct the IoT devices’ signal. An IRS is deployed to improve the energy collection and information transmission capabilities in the WET and WIT phases, respectively. To examine the performance of the system under study, We maximize the sum throughput with the aim of jointly designing the optimal solutions for the active PS energy beamforming, AP receive beamforming, passive IRS beamforming, and time scheduling. Due to the multiple coupled variables, the resulting formulation is non-convex, and a two-level scheme to solve the problem is proposed. At the outer level, a one-dimensional (1-D) search method is applied to find the optimal time scheduling, while at the inner level, an iterative block coordinate descent (BCD) algorithm is proposed to design the optimal receive beamforming, energy beamforming, and IRS phase shifts. In particular, the receive beamforming part is designed by considering the equivalence between sum rate maximisation and sum mean square error (MSE) minimisation, thereby deriving a closed-form solution. Furthermore, we alternately optimize the energy beamforming and IRS phase shifts using Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMM) methods. Finally, numerical results are presented to showcase the performance of the proposed solution and highlight its advantages compared to some typical benchmark schemes.

—In this paper, the harmony between non-orthogonal multiple access (NOMA) and rate-splitting (RS) is explored in a multi-antenna scenario, where a RS-NOMA system is proposed to implement dynamically transformation between these two transmission schemes. Specifically, a common signal is generated at the base station (BS) and transmitted with the users' private signals. By utilizing successive interference cancellation (SIC), the common signal is decoded and removed at each user, and then the private signals can be decoded based on NOMA schemes. By jointly design power allocation and user pairing, a weighted sum rate maximization problem is formulated with quality of service (QoS) constraints, where the priority of different signals is presented. By analyzing the monotonicity, the optimal power allocation is expressed in several cases, and then the strategy of each user to achieve the QoS threshold is obtained in all cases. To solve the user pairing problem, a matching based algorithm is developed, where the property is analyzed. Simulation results indicate that: i) the proposed RS-NOMA system outperforms the conventional RS and NOMA systems in general; ii) the developed power allocation strategy and user pairing algorithm can improve the RS-NOMA system in terms of the weighted sum rate and outage probability. Index Terms—Non-orthogonal multiple access (NOMA), power allocation, rate splitting (RS), user pairing

Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STARRIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ESRIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional selfinterference cancellation technology.

The separation of training and data transmission as well as the frequent uplink/downlink (UL/DL) switching make time-division duplex (TDD)-based massive multiple-input multiple-output (mMIMO) systems less competent in fast time-varying scenarios due to the resultant severe channel aging. To this end, a multicarrier-division duplex (MDD) mMIMO scheme associated with two types of well-designed frame structures are introduced for combating channel aging when communicating over fast time-varying channels. For comparison, the corresponding TDD frame structures related to the 3 rd Generation Partnership Project (3GPP) standards and their variant forms are presented. The MDD-specific general Wiener predictor and decision-directed Wiener predictor are introduced to predict the channel state information, respectively, in the time domain based on UL pilots and in the frequency domain based on the detected UL data, considering the impact of residual self-interference (SI). Moreover, by applying the zero-forcing precoding and maximum ratio combining, the closed-form approximations for the lower bounded rate achieved by TDD and MDD frame structures over time-varying channels are derived. Our main conclusion from this study is that the MDD, endowed with the capability of full-duplex but less demand on SI cancellation than in-band full-duplex (IBFD), outperforms both the conventional TDD and IBFD in combating channel aging.

The system under study is a convolutionally coded and orthogonally modulated DS-CDMA system in time-varying frequency selective Rayleigh fading channels. In this paper, we investigate several iterative schemes based on soft demodulation and decoding algorithms. The performance of different strategies are evaluated numerically and proved to achieve substantial performance gain compared to the conventional hard decision based scheme, especially when the soft demodulator is assisted by decision directed channel estimation and interference cancellation techniques, and also when demodulation and decoding are performed jointly in an iterative manner.

In this paper, an 8×8 Multiple Input Multiple Output (MIMO) antenna design for Fifth Generation (5G) sub- 6GHz smartphone applications is presented. The antenna elements are based off a folded quarter wavelength monopole that operate at 3.4-3.8GHz. Isolation between antenna elements is provided through physical distancing. The fabricated antenna prototype outer casing is made from Rogers R04003C with dimensions based on future 5G enabled phones. Measured results show an operating bandwidth of 3.32 to 3.925GHz (S11 < 6dB) with a transmission coefficient < -14.7dB. A high total efficiency for an antenna array is also obtained at 70-85.6%. The design is suitable for MIMO communications exhibited by an Envelope Correlation Coefficient (ECC) < 0.014. To conclude a Specific Absorption Rate (SAR) model has been constructed and presented showing the user’s effects on the antenna’s Sparameter results. Measurements of the amount of power absorbed by the head and hand during operation have also been simulated.

This paper investigates data transmission and physical layer secrecy in cognitive radio network. We propose to apply full duplex transmission and dual antenna selection at secondary destination node. With the full duplex transmission, the secondary destination node can simultaneously apply the receiving and jamming antenna selection to improve the secondary data transmission and primary secrecy performance respectively. This describes an attractive scheme in practice: unlike that in most existing approaches, the secrecy performance improvement in the CR network is no longer at the price of the data transmission loss. The outage probabilities for both the data transmission and physical layer secrecy are analyzed. Numerical simulations are also included to verify the performance of the proposed scheme.

In this paper, non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) is investigated, where the users are grouped into multiple clusters. Exploiting conjugate beamforming, the bandwidth efficiency (BE) of the system is derived while the assumption that the users performing realistic successive interference cancellation (SIC) based on only the knowledge of channel statistics. The max-min fairness problem of maximizing the lowest user BE is investigated and an iterative bisection method is developed to determine the optimal solution to the max-min BE problem. Numerical results are presented for validating the proposed design’s performance, and a mode switching scheme is conceived for selecting a specific Mode = f OMA, NOMA g that maximizes the system’s BE.

A novel fine timing synchronizaton method based on the modified expectation-maximization (EM) clustering algorithm is proposed for OFDM systems. Using the cross-correlation metrics of one preamble symbol, the cross-correlation peaks corresponding to the channel arriving paths are identified by the proposed modified EM clustering algorithm, the position of the first coherent cross-correlation peak is then chosen as the start of the frame. Computer simulations show that the proposed method is robust in multipath dispersive channels and achieves superior performance to existing techniques in terms of timing accuracy.

A cell-free Massive multiple-input multiple-output (MIMO) system is considered, where the access points (APs) are linked to a central processing unit (CPU) via the limited-capacity fronthaul links. It is assumed that only the quantized version of the weighted signals are available at the CPU. The achievable rate of a limited fronthaul cell-free massive MIMO with local minimum mean square error (MMSE) detection is studied. We study the assumption of uncorrelated quantization distortion, which is commonly used in literature. We show that this assumption will not affect the validity of the insights obtained in our work. To investigate this, we compare the uplink per-user rate with different system parameters for two different scenarios; 1) the exact uplink per-user rate and 2) the uplink per-user rate while ignoring the correlation between the inputs of the quantizers. Finally, we present the conditions which imply that the quantization distortions across APs can be assumed to be uncorrelated.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the desired radar waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is employed to determine the communication beamforming vector and the IRS phase shift. For computing the IRS phase shift, we adopt both the majorization minimization (MM) and the manifold optimization (MO). Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Furthermore, a trade-off factor is introduced to balance the performance of communication and sensing. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.

In this paper, an ultra-wideband, Dielectric Resonator Antenna (DRA) has been proposed. The proposed antenna is based on isosceles triangular DRA (TDRA), which is fed from the base side using a 50Ω probe. For bandwidth enhancement and radiation characteristics improvement, a partially cylindrical-shape hole is etched from its base side which approached probe feed to the center of TDRA. The dielectric resonator (DR) is located over an extended conducting ground plane. This technique has significantly enhanced antennas bandwidth from 48.8% to 80% (5.29-12.35 GHz), while the biggest problem was radiation characteristics. The basis antenna possesses negative gain in a wide range of bandwidth from 7.5 GHz to 10.5 GHz down to -13.8 dBi. Using this technique improve antenna gain over 1.6 dBi for whole bandwidth, while peak gain is 7.2 dBi.

Utilizing the holography theory, a bidirectional wideband leaky wave antenna in the millimetre wave (mmW) band is presented. The antenna includes a printed pattern of continuous metallic strips on an Alumina 99:5% sheet, and a surface wave launcher (SWL) to produce the initial reference waves on the substrate. To achieve a bidirectional radiation pattern, the fundamental TE mode is excited by applying a Vivaldi antenna (as the SWL). The proposed holographic-based leaky wave antenna (HLWA) is fabricated and tested and the measured results are aligned with the simulated ones. The antenna has 22:6% fractional bandwidth with respect to the central frequency of 30 GHz. The interference pattern is designed to generate a 15 deg backward tilted bidirectional radiation pattern with respect to the normal of the hologram sheet. The frequency scanning property of the designed HLWA is also investigated.

—In this paper, we propose a method to construct codebooks for downlink sparse code multiple access (SCMA) systems. Different from the traditional methods in which codebooks are designed for each user, we allocate codebooks for each sub-channel based on the most common modulation QAM. Then, low error probability criteria to measure the SCMA codebook performance in AWGN channel and Rayleigh fading channel are proposed. And simulated annealing algorithm is used to optimize the codebooks based on the criteria. Simulation results prove that the proposed codebooks exhibit good performance in both Rayleigh fading channel and AWGN channel, especially for high order SCMA systems.

This paper investigates the performance of limited-fronthaul cell-free massive multiple-input multiple-output (MIMO) taking account the fronthaul quantization and imperfect channel acquisition. Three cases are studied, which we refer to as Estimate&Quantize, Quantize&Estimate, and Decentralized, according to where channel estimation is performed and exploited. Maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) receivers are considered. The Max algorithm and the Bussgang decomposition are exploited to model optimum uniform quantization. Exploiting the optimal step size of the quantizer, analytical expressions for spectral and energy efficiencies are presented. Finally, an access point (AP) assignment algorithm is proposed to improve the performance of the decentralized scheme. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting relatively few quantization bits, the performance of limited-fronthaul cell-free massive MIMO closely approaches the perfect-fronthaul performance.

Orthogonal frequency division multiplexing (OFDM) with index modulation (IM) (OFDM-IM), which employs the activated sub-carrier indices to convey information, exhibits higher energy efficiency and lower peak-to-average power ratio (PAPR)thanconventionalOFDMsystems.Tofurtherimprovethe throughput of discrete Fourier transform (DFT) based OFDM-IM (DFT-OFDM-IM),discretecosinetransform(DCT)basedOFDMIM (DCT-OFDM-IM) can be employed with double subcarriers giventhesamebandwidth.However,oneofthemaindisadvantage of DCT-OFDM-IM is its lack of circular convolutional property over a dispersive channel. To address this issue, an enhanced DCT-OFDM-IM(EDCT-OFDM-IM)systemhasbeenproposedby introducing symmetric prefix and suffix at the transmitter and a pre-filter at the receiver leading to better performance than DFTOFDM-IM in terms of bit error rate (BER). However, due to its special structure, it is difficult to derive the accurate absolute bit error probability (ABEP) upper bound, which is essential for the performance evaluation. In this paper, a tight ABEP upper bound is derived using the moment-generating-function (MGF). Our theoretical analysis is validated by simulation results and proven to be very accurate. Consequently the advantages of the EDCT-OFDM-IM system over the classic OFDM-IM system are further demonstrated analytically.

In this paper, we aim to unlock the potential of intelligent reflecting surfaces (IRSs) in cognitive internet of things (IoT). Considering that the secondary IoT devices send messages to the secondary access point (SAP) by sharing the spectrum with the primary network, the interference is introduced by the IoT devices to the primary access point (PAP) which profits from the IoT devices by pricing the interference power charged by them. A practical path loss model is adopted such that the IRSs deployed between the IoT devices and SAP serve as diffuse scatterers, but each reflected signal can be aligned with its own desired direction. Moreover, two transmission policies of the secondary network are investigated without/with a successive interference cancellation (SIC) technique. The signal-to-interference plus noise ratio (SINR) balancing is considered to overcome the near-far effect of the IoT devices so as to allocate the resource fairly among them. We propose a Stackelberg game strategy to characterize the interaction between primary and secondary networks. For the proposed game, the Stackelberg equilibrium is analytically derived to optimally obtain the closed-form solution of the power allocation and interference pricing. Numerical results are demonstrated to validate the performance of the theoretical derivations.

In this paper, we propose a novel iterative receiver strategy for uncoded multiple-input, multiple-output (MIMO) systems employing improper signal constellations. The proposed scheme is shown to achieve superior performance and faster convergence without the loss of spectrum efficiency compared to the conventional iterative receivers. The superiority of this novel approach over conventional solutions is verified by both simulation and analytical results.

Time synchronization algorithms for OFDM systems using the short and long training symbols are investigated in this paper. We only consider efficient low complexity schemes that are feasible for practical implementations. Different algorithms are compared in the context of IEEE 802.11Wireless Local Area Network (WLAN) systems. Based on the simulation results, some recommendations are made as to how the short and long training symbols can be effectively utilized for synchronization purposes.

Generalized Spatial Modulation (GSM), where both the Transmit Antenna Combination (TAC) index and the Amplitude Phase Modulation (APM) symbols convey information, is a novel low-complexity and high efficiency Multiple Input Multiple Output (MIMO) technique. In Conventional GSM (C-GSM), the legitimate TACs are selected randomly to transmit the APM symbols. However, the number of the TACs must be a power of two, hence the excess TACs are discarded, resulting in wasting some resource. To address these issues, in this paper, an optimal TAC set-aided Enhanced GSM (E-GSM) scheme is proposed, where the optimal TAC set is selected with the aid of the Channel State Information (CSI) by maximizing the Minimum Euclidean Distance (MED). Furthermore, a Hybrid Mapping GSM (HM-GSM) scheme operating without CSI knowledge is investigated, where the TAC selection and bit-to-TAC mapping are both taken into consideration for optimizing the Average Hamming Distance (AHD). Finally, an Enhanced High Throughput GSM (E-HT-GSM) scheme is developed, which makes full use of all the TACs. This scheme is capable of achieving an extra one bit transmission rate per time slot. Moreover, rotated phase is employed and optimized for the reused TACs. Our simulation results show that the proposed E-GSM system and HM-GSM system are capable of outperforming the CGSM system. Furthermore, the E-HT-GSM system is capable of obtaining one extra bit transmission rate per time slot compared to the C-GSM system.

This communication proposes a compact, low-profile patch antenna with omni-directional radiation pattern and vertical polarization. A pair of shorted patches are excited in-phase to achieve the omni-directivity and the vertical polarization, simultaneously. The principle is to excite two back-to-back arranged shorted patches to generate symmetrical electric field (E-field) distributions normal to the ground plane. Analytical study on how to generate the omni-directional radiation pattern is carried out. Base on this study, we found the spacing in-between the two patches have little influence on the radiation characteristics, which provides another flexibility in the design. In addition, the shape of the patch and the corresponding field distribution are investigated to further improve the omni-directivity. To improve the impedance bandwidth, resonant structures are inserted in-between the patches, producing the 2nd order response in frequency. The antenna has been fabricated and characterized, and the measured results are in a reasonable agreement with the simulations, showing that the proposed antenna is suitable for potential surface-mount wireless applications.

"The authors propose some robust adaptive multiuser detection schemes for direct-sequence code-division multipleaccess multipath frequency-selective fading channels. Multiple access interference (MAI) and intersymbol interference (ISI) are presented in an identical format using expanded signal subspace, which facilitates multiuser detection in a symbol-bysymbol fashion. This study contributes to the theoretical aspect of adaptive multiuser detection by proving that the optimum linear multiuser detectors that achieve maximum signal-to-interference-plus-noise ratio (SINR) must exist in the signal subspace, and the theoretic SINR upper bound is also derived. Another contribution of this study is to propose the design of multiuser detectors in an expanded signal subspace, and introduce subspace estimation and Kalman filtering algorithms for their adaptive implementation. To robustify the adaptive detectors against subspace estimation and channel estimation errors, a modified projection approximation subspace tracking (PAST) algorithm is proposed for subspace tracking. It is demonstrated by simulations that these adaptive detectors effectively suppress both MAI and ISI and converge to the optimum SINR. They are robust against subspace estimation errors and channel estimation errors compared to the conventional Wiener minimum mean square error (MMSE) detector."

In this contribution, the concepts of polarization modulation (PM) and spatial modulation (SM) are integrated, to reap their respective advantages toward single-radio frequency (RF) multiple-input multiple-output (MIMO) transmissions. In the so-called polarization and spatial modulation (PSM) system, the information is conveyed by activated antenna indices as well as polarization modulated symbols, while at the receiver, a low-complexity near-optimal detection algorithm based on compressive sensing (CS) is proposed. Furthermore, a closed-form union bound of the average bit-error rate (BER) over fading channels is quantified by theoretical derivation, which is then extended to the case of spatial correlation (SC) as well as channel estimation error (CSE) toward practical use. Finally, our simulation results demonstrate the superiority of the developed PSM system over conventional PM in terms of BER performance over various fading channels.

The improper nature of intersymbol interference (ISI) for signals transmitted over frequency-selective channels is investigated in this paper. Our analysis reveals that for real signals, the improperness originates from both improper signal modulation and the interference cancellation process, whereas for most complex signals, the improperness is only a characteristic of the residual ISI due to interference cancellation. To utilize the improperness of ISI, a multistage widely linear equalization algorithm is introduced, and it is generally applicable for both real and complex signal constellations. The results reveal that accounting for the improper nature of the ISI at both the input and output of the equalizer leads to a noticeable performance gain compared with conventional equalization schemes.

An iterative turbo decoder based cross layer error recovery scheme for compressed video is presented in this paper. The soft information exchanged between two convolutional decoders are reinforced both by channel coded parity and video compression syntactical information. An algorithm to identify the video frame boundaries in corrupted compressed sequences is formulated. The paper continues to propose algorithms to deduce the correct values for selected fields in the compressed stream. Modifying the turbo extrinsic information using these corrections act as reinforcements in the turbo decoding iterative process. The optimal number of turbo iterations suitable for the proposed system model is derived using EXIT charts. Simulation results reveal that a transmission power saving of 2.28% can be achieved using the proposed methodology. Contrary to typical joint cross layer decoding schemes, the additional resource requirement is minimal since the proposed decoding cycle does not involve the decompression function.

In this paper, empty substrate integrated waveguides (ESIW) technology is applied to design long slot leaky-wave antennas (LWAs). First, a uniform-aperture structure is presented and its limitations on forming the beam are studied. Then, a sinusoidal curve is employed to modify the geometry of guided-wave structure which divides the slot into a number of segments, making a periodic aperture. After that, a method is proposed to regulate the guided waves inside the ESIW. To this end, a modulation function is derived to simultaneously determine the local amplitude and segment length of the physical sinusoidal curve at each individual points on the structure. This results in manipulating the phase constant (_) and leakage rate (_) across the aperture which ultimately controls both the tilt angle and side-lobe-level (SLL) of the constructed beam. The slot is placed on the centerline of the broad wall of the ESIW in order to reduce the cross polarization. The structure is designed to operate at 35 GHz with SLL = 􀀀30 dB and a backward tilt angle of _m = 􀀀20 deg. Finally, the proposed LWA is simulated and a fabricated design is measured. A good agreement is observed between the theoretical, simulated, and measured performance of the antenna.

This paper investigates the impact of unreliable backhaul and channel estimation error on the performance of small-cell networks over independent and identically distributed Nakagami-m fading channels. To overcome the impact of these practical constraints, we propose an optimal selection scheme where the best small cell with respect to the maximal secrecy capacity is selected. The secrecy outage probability for the considered scheme is derived and compared with Monte-Carlo simulations. To gain additional insights on the impact of unreliable backhaul and imperfect channel estimation, the asymptotic behaviour of secrecy outage probability is also obtained.

In this paper, we consider an edge cache-assisted millimeter wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can prefetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their required files either from the local caches or from the central processor via multicast fronthaul links. For such a scenario, we formulate a beamforming design problem to minimize the secure transmission delay under transmit power constraint of each RRH. Due to the diffculty of directly solving the formulated problem, we divide it into two independent ones: i) minimizing the fronthaul transmission delay by jointly optimizing the transmit and receive beamforming; ii) minimizing the maximum access transmission delay by jointly designing cooperative beamforming among RRHs. An alternatively iterative algorithm is proposed to solve the first optimization problem. For the latter, we first design the analog beamforming based on the channel state information of the actuators. Then, with the aid of successive convex approximation and S -procedure techniques, a semidefinite program (SDP) is formulated, and an iterative algorithm is proposed through SDP relaxation. Finally, simulation results are provided to verify the performance of the proposed schemes.

A novel high-isolation dual-polarized in-band full-duplex (IBFD) dielectric resonator antenna (DRA) for satellite communications using a decoupling structure is proposed. Good isolation between transmit and receive ports is achieved by placing two identical linearly polarized resonators orthogonal to each other. Each resonator consists of a main rectangular dielectric resonator of the dielectric constant of 10 and is loaded by a thin dielectric slab of lower permittivity of 5 to broaden the matching bandwidth further. The isolation is further improved by loading an absorber and etching several slots in the ground plane. Finally, the proposed DRA is fabricated and measured to validate the concepts. Measurement results show high isolation of more than 50 dB over the desired operating bandwidth from 23.04 GHz to 24.08 GHz (ka-band) with a peak gain of about 8.93 dBi and 8.09 dBi for Port 1 and Port 2, respectively. In addition, the proposed IBFD DRA provides 11.87 GHz and 4.84 GHz isolation bandwidths over 25 dB and 30 dB, respectively, making it a potential candidate for mm-wave terrestrial applications.

—This paper investigates the maximal achievable rate for a given maximal error probability, and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength and finite alphabet constraints channel coding achievability and converse bounds based on the Berry-Esseen theorem, the Mellin transform and the closed-form expression of the mutual information and the unconditional variance. The numerical evaluation shows a fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength. Index Terms—RIS, MIMO, finite blocklength, achievable rate, achievablility bound, converse bound.

The concept of massive spatial modulation (SM) assisted vertical bell labs space-time (V-BLAST) (SM-VBLAST) system [1] is proposed, where SM symbols (instead of conventional constellation symbols) are mapped onto the VBLAST structure. We show that the proposed SM-VBLAST is a promising massive multiple input multiple output (MIMO) candidate owing to its high throughput and low number of radio frequency (RF) chains used at the transmitter. For the generalized massive SM-VBLAST systems, we first derive both the upper bounds of the average bit error probability (ABEP) and the lower bounds of the ergodic capacity. Then, we develop an efficient error correction mechanism (ECM) assisted compressive sensing (CS) detector whose performance tends to achieve that of the maximum likelihood (ML) detector. Our simulations indicate that the proposed ECM-CS detector is suitable both for massive SM-MIMO based point-to-point and for uplink communications at the cost of a slightly higher complexity than that of the compressive sampling matching pursuit (CoSaMP) based detector in the high SNR region.

The 5th generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%.

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for low-power wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming and uplink multi-user detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penalty-based optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

This paper investigates the negative impact of spatial fading correlation in multiple-input multiple-output (MIMO) relaying systems on the performance of energy beamforming. Namely, a source and destination nodes equipped with multiple antennas which have a general correlation structures and arbitrary eigenvalue multiplicities, exchanging information through a dual-hop amplify-and-forward (AF) single antenna energyconstrained relay. To facilitate longer-distance wireless power transfer, the overall scavenged energy needs to be maximized. Hence, the energy-constrained relay harvest energy from the source radio-frequency (RF) signal through energy beamforming, the harvested energy is then used to forward the source information symbol to the destination. The time switching-based receiver (TSR) along with the power splitting-based receiver (PSR) protocols are examined in order to perform wireless information and power transfer at the relay. To this end, tight closed-form lower and upper bounds for the outage probability and ergodic capacity are derived, and used to examine the throughput of the delay-constrained and delay-tolerant transmission modes, respectively. Numerical results supported by simulations manifest the tightness of the presented analytical formulas. The effect of several parameters like energy harvesting ratio, source transmit power, number of antennas and spatial fading correlation on the overall throughput is investigated. It is shown that increasing the number of antennas could be used to improve the system throughput or facilitate longer-distance wireless power transfer. On the other hand, the ramification of spatial correlation on the system throughput is also studied for arbitrary correlation structure. Moreover, it is revealed that the performance of powersplitting receiver outperforms the time-switching receiver at high signal-to-noise ratio. Finally, simulation results for the case of statistical CSI is also included for comparison purposes.

The uplink of a cell-free massive multiple-input multiple-output with maximum-ratio combining (MRC) and zero-forcing (ZF) schemes are investigated. A power allocation optimization problem is considered, where two conflicting metrics, namely the sum rate and fairness, are jointly optimized. As there is no closed-form expression for the achievable rate in terms of the large scale-fading (LSF) components, the sum rate fairness trade-off optimization problem cannot be solved by using known convex optimization methods. To alleviate this problem, we propose two new approaches. For the first approach, a use-and-then-forget scheme is utilized to derive a closed-form expression for the achievable rate. Then, the fairness optimization problem is iteratively solved through the proposed sequential convex approximation (SCA) scheme. For the second approach, we exploit LSF coefficients as inputs of a twin delayed deep deterministic policy gradient (TD3), which efficiently solves the non-convex sum rate fairness trade-off optimization problem. Next, the complexity and convergence properties of the proposed schemes are analyzed. Numerical results demonstrate the superiority of the proposed approaches over conventional power control algorithms in terms of the sum rate and minimum user rate for both the ZF and MRC receivers. Moreover, the proposed TD3-based power control achieves better performance than the proposed SCA-based approach as well as the fractional power scheme.

Channel estimation for multiple-input, multiple-output (MIMO) systems is studied in this paper. In particular, we present a simplified MIMO channel estimator based on orthogonal design. The performance of the proposed scheme is theoretically analyzed and compared to that of the optimum maximum likelihood estimator. The effect of non-orthogonality of the training sequences is investigated. Some modifications of the proposed estimator with sample stacking and averaging are introduced to further improve the estimation performance. This simplified scheme is evaluated in the context of the WiMAX MIMO systems in terms of mean square error for the channel estimation and bit error rate for the space-time turbo equalization. Both analytical and simulation results indicate that despite of its low computational complexity, this simplified estimator leads to minimum variance unbiased estimation and achieves identical performance to that of the maximum likelihood estimator.

In this paper, a novel index and composition modulation (ICM) transmission scheme, termed as grouped generalized composition and spatial modulation (G-GCSM), is proposed for massive multiple-input multiple-output (MIMO) systems. Specifically , it amalgamates the concepts of composition modulation (CM), generalized spatial modulation (GSM) and spatial multi-plexing to attain high spectral efficiency (SE) and low implementation complexity. In the G-GCSM scheme, transmit antennas are divided into several groups and the GCSM transmission structure is employed independently in each group, facilitating the bit-to-index mapping issue in massive MIMO scenarios. Additionally, at the receiver side, an improved expectation propagation (EP) detector is designed for the proposed G-GCSM scheme, which exploits the inner sparsity of the transmitted vector in G-GCSM. Simulation results demonstrate the superiority of the proposed scheme over the existing GSM schemes in terms of bit error rate (BER) performance under the same SE conditions. Moreover, the proposed improved EP detector is able to provide a significant performance gain over the conventional minimum-mean-squared error (MMSE) detector in both determined and under-determined massive MIMO systems.

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combinequantize- and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing largescale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

This paper proposes a new iterative frequency domain equalization (FDE) algorithm for multiple-input multipleoutput (MIMO)-frequency division multiplexing (GFDM) systems. This new FDE scheme is capable of enhancing the system fidelity by considering the complete frequency-domain second order description of the received signal. In addition, a new nulling filter design is also proposed for MIMO-GFDM systems to remove the residual interference, which further improves the system fidelity compared to the traditional scheme. Simulation results are presented to verify the effectiveness and efficiency of the proposed FDE algorithm.

This paper analyzes the maximal achievable rate for a given blocklength and maximal error probability over a multiple-antenna ambient backscatter channel. The result consists of a finite blocklength channel coding achievability bound and a converse bound for the legacy system with finite alphabet constraints and multiple-input-multiple-output based on the Neyman-Pearson test, the Berry-Esseen theorem, and the Mellin transform. Then, we derive the closed-form expression of the mutual information and the information variance to reduce the complexity of the computation. By applying the low-complexity ML detection, the relation between the maximal error probability of the RF source signal and the average error probability of the tag symbol with respect to the blocklength is proposed. Finally, numerical evaluation of these bounds shows fast convergence to the maximal achievable rate as the blocklength increases and also proves that the information variance is an accurate measure of the backoff from the maximal achievable rate due to finite blocklength.

To make indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks free from wired fronthaul, this paper studies a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. More specifically, two layers of fronthaul links rely on the mutually orthogonal subcarreir sets in the same THz band, while access links are implemented over sub-6G band. The proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets between two fronthaul links and the resource allocation at both the central processing unit (CPU) and APs. In order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the binary variables. Then, the overall end-to-end rate is obtained by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the varying sizes of networks. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that with sufficient frequency resources, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations.

In this article, we investigate a resource allocation problem for multicarrier multiuser MISO (multiple-input-Single-output) downlink systems, where multiple co-channel multicast groups are served simultaneously. We consider a rate-splitting transmission scheme to address the inevitable inter-group interference under an overloaded multigroup multicast scenario, where the insufficient number of transmit antennas prevents the conventional schemes from neutralizing the interference. We first formulate an optimization problem for maximizing the minimum multicast group rate among all groups on all available subcarriers. This problem involves a joint power and subcarrier allocation optimization, and is non-convex. We apply an iterative scheme based on successive convex approximation (SCA) to find the locally optimal solution. Simulation results demonstrate the performance gain of the proposed scheme compared to the state-of-the-art transmission schemes.

In the paper, we present a road-map towards a Nearcapacity Large-scale Multi-user Cooperative-communications (NLMC) system, where all the evolution paths converge to the construction of the NLMC system. More specifically, we will summarise all relevant schemes appearing on the road-map in the unified frame-work of forward error correction (FEC). Various Network Coding (NC) design paradigms are highlighted for illustrating how the NLMC systems might be designed for meeting diverse design criteria in the context of cooperative and cognitive communications, where the channel capacity of the NLMC systems is used for comparing the different design paradigms.

This manuscript presents a novel approach for designing wideband omnidirectional slotted-waveguide antenna arrays, which is based on trapezoidal-shaped slots with two different electrical lengths, as well as a twisted distribution of slot groups along the array longitudinal axis. The trapezoidal section is formed by gradually increasing the slot length between the waveguide interior and exterior surfaces. In this way, a smoother impedance transition between waveguide and air is provided in order to enhance the array operating bandwidth. Additionally, we propose a twisting technique, responsible to improve the omnidirectional pattern, by means of reducing the gain ripple in the azimuth plane. Experimental results demonstrate 1.09 GHz bandwidth centered at 24 GHz (4.54% fractional bandwidth), gain up to 14.71 dBi over the operating bandwidth and only 2.7 dB gain variation in the azimuth plane. The proposed antenna array and its enabling techniques present themselves as promising solutions for mm-wave application, including 5G enhanced mobile broadband (eMBB) communications.

This contribution introduces a framework for the fault detection and healing of chemical processes over wireless sensor networks. The approach considers the development of a hybrid system which consists of a fault detection method based on machine learning, a wireless communication model and an ontology-based multi-agent system with a cooperative control for the process monitoring.

The systems under study are broadband wireless fixed access (BFWA) systems over multipath fading channels. Conventional detection methods like coherent and non-coherent detection are examined theoretically for both QPSK and 16-QAM modulated BFWA systems in this paper and shown to yield unsatisfactory performance. The theoretical analysis for different algorithms are validated by Monte-Carlo simulations and proved to be accurate. They give us an insight into the physical limitations of the BFWA channels and suggest solutions to improve the capacity and performance of future BFWA systems.

This paper presents a novel design of a high-gain omnidirectional slotted-waveguide antenna array for 5G mmwave applications. The structure is based on a circular waveguide filled with teflon for manipulating its dimension. It provides 12,1 dBi gain and omnidirectional coverage in the azimuth plane with only 1.3 dB deviation, which is ensured by making use of a twisting technique for proper placing the slots into the waveguide walls. A bandwidth of 1.61 GHz centered at 26.2 GHz has been numerically demonstrated.

In this paper, we theoretically investigate the performance of non-orthogonal and orthogonal spectrum access protocols (more generically known as NOMA) in supporting ultra-reliable low-latency communications (URLLC). The theory of effective capacity (EC) is adopted as a suitable delayguaranteed capacity metric to flexibly represent the users’ delay requirements. Then, the total EC difference between a downlink user-paired NOMA network and a downlink orthogonal multiple access (OMA) network is analytically studied. Exact closed-form expressions and the approximated closed-forms at high signal-tonoise ratios (SNRs) are derived for both networks and validated through simulation results. It is shown that for a user pair in which two users with the most distinct channel conditions are paired together, NOMA still achieves higher total EC (compared to OMA) in high SNR regime as the user group size becomes larger, although the EC performance of both NOMA and OMA reduces with the increase in group size. It is expected that the derived analytical framework can serve as a useful reference and practical guideline for designing favourable orthogonal and nonorthogonal spectrum access schemes in supporting low-latency services.

In this paper, a novel differential space-time block coded spatial modulation (differential STBC-SM) is proposed for uplink multi-user massive multiple-input multiple-output (MIMO) communications, which combines the concept of differential coding and STBC-SM to enhance the diversity benefits in the absence of the channel state information (CSI). The transmission structure of the proposed system is on a block basis, where each block contains two sub-blocks. More specifically, the first sub-block only conveys amplitude and phase modulation (APM) symbol bits, since its transmit antennas (TAs) obey a pre-designed activation pattern, which do not carry any information bit. For the second sub-block, the input bits are modulated to STBCSM matrices, which are then differentially coded between two adjacent sub-blocks. Moreover, a novel block-by-block based non-coherent detector is presented. Finally, we derive an upper bound on the average bit error probability (ABEP) by using the moment generating function (MGF). Our simulation results show that the proposed differential STBC-SM transmission structure is able to acquire considerable bit error rate (BER) performance improvements compared to both the conventional differential spatial modulation (DSM) and differential Alamouti schemes.

Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 121.7% higher than the selfish algorithm.

This paper studies the impact of an intelligent reflecting surface (IRS) on computational performance in a mobile edge computing (MEC) system. Specifically, an access point (AP) equipped with an edge server provides MEC services to multiple internet of thing (IoT) devices that choose to offload a portion of their own computational tasks to the AP with the remaining portion being locally computed. We deploy an IRS to enhance the computational performance of the MEC system by intelligently adjusting the phase shift of each reflecting element. A joint design problem is formulated for the considered IRS assisted MEC system, aiming to optimize its sum computational bits and taking into account the CPU frequency, the offloading time allocation, transmit power of each device as well as the phase shifts of the IRS. To deal with the non-convexity of the formulated problem, we conduct our algorithm design by finding the optimized phase shifts first and then achieving the jointly optimal solution of the CPU frequency, the transmit power and the offloading time allocation by considering the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical evaluations highlight the advantage of the IRS-assisted MEC system in comparison with the benchmark schemes.

The book examines several aspects of Orthogonal Frequency Division Multiplexing (OFDM) employing linear diversity techniques such as inter-carrier interference, bit error rate, peak to average power and inter-block interference.

A multifunctional antenna with diverse radiation patterns in different frequency bands (2.45/5.8 GHz) is presented in this paper. The antenna has a low profile but exhibits an omni-directional radiation pattern in the low-band operation and uni-directional pattern in the high-band operation. For the high-band operation, a 2 × 2 patch arrays are designed by employing a out-of-phase feeding method. The low-band operation with the omni-directional pattern is achieved by exciting four open-ended slots in-phase. The four slots are slit in the ground of the high-band array and in this way, this footprint of the antenna is maintained. The operating principles of the antenna are studied with the aid of equivalent circuit model and the current distribution. The antenna is prototyped and measured, demonstrating good results in terms of bandwidths, inter-channel isolation, radiation characteristics.

This study examines the effect of different pressures on the radiation characteristics of the loop-shaped plasma antenna filled by two gases; Argon and Nitrogen. Proposed loop plasma antennas operating at LTE and Wi-Fi frequency bands have been designed and its performance studied at three different pressures of 2.28, 5 and 10 Torr. The radiation characteristics of the both loop-shaped plasma antennas have been investigated and presented for three different pressures. To analyze the performance of the proposed antenna, full-wave simulation were run using the finite integral method software, CST Microwave Studio.

The problem of estimating propagation delays of orthogonally modulated signals in asynchronous DS-CDMA systems over time-varing Rayleigh-fading channels is treated in this paper. The maximum likelihood (ML) estimator and its unaffordable complexity for implementation are discussed. Some suboptimal solutions, e.g., whitened sliding correlator, MMSE estimator, subspace-based estimator, approximate ML estimator, are proposed to combat the multiple access interference in the fading channels. The performance of these estimators are evaluated with the computer simulations and shown to have better acquisition performance than the standard sliding correlator. They also achieve reduced computational complexity compared with the ML estimator, while maintaining an acceptable performance degradation.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has attracted considerable interest recently. The technique uses the subcarrier indices as a source of information. In FBMC system, doubledispersive channels lead to inter-carrier interference (ICI) and/or inter-symbol interference (ISI), which are caused by the neighboring symbols in the frequency and/or time domain. When we introduce index modulation to the FBMC system, the interference power will be smaller comparing to that of the conventional FBMC system as some subcarriers carry nothing but zeros. In this paper, the advantages of FBMC with index modulation (FBMC-IM) are investigated by comparing the signal to interference ratio (SIR) with that of the conventional FBMC system. However, the bit error rate (BER) performance is affected since there exists interference in the FBMC-IM system. To improve the BER performance, we propose an optimal combination-selection algorithm and an optimal combinationmapping rule. By abandoning some combinations whose error probability are larger and by mapping the remaining combinations into specified bits, a better BER performance can be achieved compared with that without optimization. The theoretical analysis and simulation results clearly show the FBMC-IM system has a good BER performance under double-dispersive channels.

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semidefinite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

Choice of a suitable waveform is a key factor in the design of 5G physical layer. New waveform/s must be capable of supporting a greater density of users, higher data throughput and should provide more efficient utilization of available spectrum to support 5G vision of “everything everywhere and always connected” with “perception of infinite capacity”. Although orthogonal frequency division multiplexing (OFDM) has been adopted as the transmission waveform in wired and wireless systems for years, it has several limitations that make it unsuitable for use in future 5G air interface. In this chapter, we investigate and analyse alternative waveforms that are promising candidate solutions to address the challenges of diverse applications and scenarios in 5G.

In this paper, we consider multigroup multicast transmissions with different types of service messages in an overloaded multicarrier system, where the number of transmitter antennas is insufficient to mitigate all inter-group interference. We show that employing a rate-splitting based multiuser beamforming approach enables a simultaneous delivery of the multiple service messages over the same time-frequency resources in a non-orthogonal fashion. Such an approach, taking into account transmission power constraints which are inevitable in practice, outperforms classic beamforming methods as well as current standardized multicast technologies, in terms of both spectrum efficiency and the flexibility of radio resource allocation.

Multiple-input multiple-output filterbank multicarrier communication (MIMO-FBMC) is a promising technique to achieve very tight spectrum confinement (thus, higher spectral efficiency) as well as strong robustness against dispersive channels. In this paper, we present a novel training design for MIMO-FBMC system which enables efficient estimate of frequency-selective channels (associated to multiple transmit antennas) with only one non-zero FBMC symbol. Our key idea is to design real-valued orthogonal training sequences (in the frequency domain) which displaying zero-correlation zone properties in the time-domain. Compared to our earlier proposed training scheme requiring at least two non-zero FBMC symbols (separated by several zero guard symbols), the proposed scheme features ultra-low training overhead yet achieves channel estimation performance comparable to our earlier proposed complex training sequence decomposition(CTSD). Our simulations validate that the proposed method is an efficient channel estimation approach for practical preamble-based MIMO-FBMC systems.

A novel reconfigurable dielectric resonator antenna (DRA) employed a T-Shaped microstrip-fed structure in order to excite the dielectric resonator is presented. By carefully adjusting the location of the inverted U-shaped slot, switches, and length of arms, the proposed antenna can support WLAN wireless system. In addition, the presented DRA can be proper for cognitive radio because of availability switching between wideband and narrowband operation. The proposed reconfigurable DRA consisting of a Roger substrate with relative permittivity 3 and a size of 20 mm × 30 mm × 0.75 mm and a dielectric resonator (DR) with a thickness of 9 mm and the overall size of 18 mm × 18 mm. Moreover, the antenna has been fabricated and tested, which test results have enjoyed a good agreement with the simulated results. As well as this, the measured and simulated results show the reconfigurability that the proposed DRA provides a dual-mode operation and also three different resonance frequencies as a result of switching the place of arms.

Several equalization algorithms utilizing the rotationally variant nature of the received signals are presented in this paper to combat the detrimental effect of intersymbol interference (ISI) introduced by frequency selective channels. Their adaptive implementations and application to a time-reversal space-time block coded (TR-STBC) system are also considered. In addition, a turbo equalization algorithm is derived for systems employing the error correction code. The proposed equalizers and turbo equalizer are evaluated over broadband fixed wireless access channels, and are shown to yield superior performance compared to the conventional equalization schemes.

In this paper, a novel beam alignment algorithm based on the sparse graph coding theory is proposed for millimeter wave (mmWave) time-varying channels. Firstly, a pilot design method is introduced to transform the mmWave timevarying beam alignment into a sparse-graph design and detection problem. Inspired by Low-Density-Parity-Check (LDPC) codes and fountain codes, a multi-stage sparse coding method is proposed for the design of the measurement matrix and the theoretical bound of the probability of success is derived to guide the design of the sparse-graph. A beam alignment algorithm is subsequently proposed to detect the beam index and estimate the carrier frequency offset (CFO). Then, the Carme´r-Rao Lower Bound (CRLB) is derived. Simulation results demonstrate that the proposed beam alignment algorithm achieves significant performance improvements over the conventional counterparts in both the noiseless and noise cases.

We consider uplink sparse code multiple access (SCMA) systems associated with multiple input multiple output (MIMO), where the transmitters and the receiver are equipped with multiple antennas, for enhanced reliability (diversity gain) or improved data rate (multiplexing gain). For each diversity or multiplexing based MIMO scheme combined with SCMA, we develop low-complexity iterative detection algorithms based on the message passing algorithm (MPA) and the expectation propagation algorithm (EPA). We show that the MIMO-SCMA under EPA enjoys the salient advantage of linear complexity (in comparison to the MPA counterpart with exponential complexity) as well as enhanced error rate performances due to the MIMO transmission. We also show that the performance of EPA depends on the codebook size and the number of antennas.

The performance of a downlink synchronous MCCDMA system with joint frequency-time domain spreading (MC-2D-CDMA) is investigated in this paper. We propose a two dimensional adaptive minimum mean square error (MMSE) receiver, which works in decision-directed mode after an initial training period. A subcarrier phase tracker, which comprises a bank of phase locked-loops (PLLs), is employed in the receiver to track the fading phase variability. Furthermore, a simplified phase tracker structure is proposed to reduce the system complexity. The performance of the data detector and the behavior of the phase tracker are analyzed theorectically in this paper and are shown to match the simulation results. Both analysis and simulation indicate that the proposed system outperforms the conventional MC-DS-CDMA systems by exploiting frequency diversity and facilitating subcarrier synchronization.

As a special case of sparse code multiple access (SCMA), low-density signatures based code-division multiple access (LDS-CDMA) was widely believed to have worse error rate performance compared to SCMA. With the aid of Eisenstein numbers, we present a novel class of LDS which can achieve error rate performances comparable to that of SCMA in Rayleigh fading channels and better performances in Gaussian channels. This is achieved by designing power-imbalanced LDS such that variation of user powers can be seen both in every chip window and the entire sequence window. As LDS-CDMA is more flexible in terms of its backwards compatibility, our proposed LDS are a promising sequence candidate for dynamic machine-type networks serving a wide range of communication devices.

Users within mobile networks require ever increasing data rates. However, the frequency spectrum, reserved for mobile networks, is highly saturated. The millimeter wave spectrum, by contrast is relatively under utilised. Nonetheless, this area of the spectrum suffers from higher propagation losses, necessitating the use of highly directional antennas. To support mobility these antennas require beam steering capabilities. For several applications wide beam scanning capability is required. A valuable approach for increasing the beam scanning range is to use element factor plus array factor control [1]. Although several authors have presented designs based on this approach the lobe performance of those antennas is generally quite poor. In this paper we seek to address that issue.

UAV-assisted wireless power transfer D2D networks have great potential to improve flexibility, spectrum efficiency and lifetime of future wireless networks. We consider an unmanned aerial vehicle (UAV)-assisted wireless powered device-to-device (D2D) wireless communication network in the presence of ground and flying eavesdroppers. We design optimization algorithms to allocate the resources of the network and maximize the secrecy energy efficiency (SEE) by optimizing energy harvesting time and power allocation. The effectiveness and viability of proposed algorithms are illustrated through numerical simulations.

This paper investigates a secure wireless powered integrated service system with full duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users, and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programmingbased two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel state information case where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

In this paper, we provide a theoretical evaluation for the multistage parallel interference cancellation (PIC) scheme in a DS-CDMA system with orthogonal modulation and long scrambling codes. The studied system operates on the reverse link in a time-varying multipath Rayleigh fading channel. Unequal powers are assumed among different paths, which is usually the case in practical situations. The proposed analysis gives insight into the performance and capacity one can expect from the PIC based receivers under different situations

The filter-based turbo equalization scheme has been proposed in several papers to avoid the prohibitive complexity imposed by the trellis-based turbo equalization. In the existing literature,the filter-based approach has been solely implemented by a linear MMSE filter, the coeffi cients of which are updated to minimize the mean-square error for every output symbol of the equalizer. A new turbo equalization algorithm is introduced in this paper. It has a lower computational complexity compared to most of the existing MMSE filter-based turbo equalization schemes. The complexity reduction is accomplished by deriving log-likelihood ratios (LLRs) directly from the output of an interference canceler, thus avoiding the MMSE filtering and its inherent matrix inversion for each symbol estimate. Numerical results show that the proposed scheme enables ISI-free transmission for some frequency selective channels.

In this paper, we developed several algorithms to combat the impact of synchronization errors on demodulating M-ary orthogonal signaling formats in asynchronous DS-CDMA systems. The system under study resembles the uplink of an IS-95 system. The channel is assumed to be a time-varying flat Rayleigh-fading channel. Investigation shows that synchronization errors severely deteriorate the performance of multi-user detectors. We proposed an adaptive algorithm to estimate the errors in synchronization. Based on this information, remedial actions are taken to alleviate the performance degradation caused by sampling the received signals at the incorrect timing. Simulation results show considerable capacity gains when the proposed algorithms are performed to erroneously sampled signals

There is a big demand for increasing number of subscribers in the fourth generation mobile communication systems. However, the system performance is limited by multi-path propagations and lack of efficient power allocation algorithms in conventional wireless communication systems. Optimal resource allocation and interference cancellation issues are critical for the improvement of system performance such as throughput and transmission reliability. In this paper, a turbo coded bell lab space time system (TBLAST) with optimal power allocation techniques based on eigen mode, Newton and convex optimization method and carrier-interference-and-noise ratio (CINR) are proposed to improve link reliability and to increase throughput with reasonable computational complexity. The proposed scheme is evaluated by Monte-Carlo simulations and is shown to outperform the conventional power allocation scheme.

This paper presents a family of training preambles for offset QAM (OQAM) based filter-bank multi-carrier (FBMC) modulations with low peak-to-average power ratio (PAPR) property. We propose to use binary Golay sequences as FBMC preambles and analyze the maximum PAPR for different numbers of zero guard symbols. For both the PHYDYAS and Hermite prototype filters with overlapping factor of 4, as an illustration of the proposed preambles, we show that a preamble PAPR less than 3 dB can be achieved with probability of one, when three or more zero guard symbols are inserted in the vicinity of each preamble.

This paper reports a reconfigurable intelligent surface (RIS) for beamforming and beam steering applications operating in the millimeter wave (mm-waves) frequency band. The proposed 2-bit RIS design is implemented using a radar cross-section (RCS) approach in ANSYS HFSS for performance evaluation and system-level analysis. It is based on split-ring resonator (SRR) unit cells, tuned by varactor diodes, comprising 1024 elements arranged in a 32x32 matrix with linear gradient phase configuration operating at 24.5 GHz over the fifth generation of mobile communications New Radio (5G NR) frequency range 2 (FR2). A beam steering from -60° to 60° in the azimuth plane is demonstrated for mm-waves coverage extension. Numerical simulations of RCS patterns from -10° to -60° and from 10° to 60° with approximately 3 dB scan loss manifest the applicability of the proposed RIS towards the sixth generation of mobile communications (6G). Furthermore, simulated results of angular reciprocity demonstrate a RIS response up to 110º under an oblique incident wave at 60°. To the best of our knowledge, this is the highest RIS angular reciprocity reported in literature, validating its application to coverage extension from -60° to 60°. In addition, the relation between RCS level and reflected angle is used for system-level analyses.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at https://github.com/ethanlq/SCMA-codebook.

Non-orthogonal Multiple Access (NOMA) has been envisioned as one of the key enabling techniques to fulfill the requirements of future wireless networks. The primary benefit of NOMA is higher spectrum efficiency compared to Orthogonal Multiple Access (OMA). This paper presents an error rate comparison of two distinct NOMA schemes, i.e., power domain NOMA (PD-NOMA) and Sparse Code Multiple Access (SCMA). In a typical PD-NOMA system, successive interference cancellation (SIC) is utilized at the receiver, which however may lead to error propagation. In comparison, message passing decoding is employed in SCMA. To attain the best error rate performance of PD-NOMA, we optimize the power allocation with the aid of pairwise error probability and then carry out the decoding using generalized sphere decoder (GSD). Our extensive simulation results show that SCMA system with " 5\times 10 " setting (i.e., ten users communicate over five subcarriers, each active over two subcarriers) achieves better uncoded BER and coded BER performance than both typical " 1\times 2 " and " 2\times 4 " PD-NOMA systems in uplink Rayleigh fading channel. Finally, the impacts of channel estimation error on SCMA, SIC and GSD based PD-NOMA and the complexity of multiuser detection schemes are also discussed.

Conference Title: 2021 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM) Conference Start Date: 2021, Nov. 28 Conference End Date: 2021, Nov. 30 Conference Location: Guangzhou, ChinaRecent deployments of satellite constellations have taken an increased interest in Low Earth Orbit (LEO) owing to the advantages of lower path loss, lower latency in terms of the propagation delay and coverage of concentrated pockets of area. Due to the nature of these orbits, antennas on the ground station with the ability to scan over wide angles is necessitated. Phased array antennas are the ideal candidates in such a scenario owing to their low cost, low profile and wide-angle scanning performance. In this paper, a phased array antenna is designed and presented to operate around the n261 band of 5GNRFR2 and the 28 GHz Ka-band with an azimuthal beamscanning range of 50° on either side of the boresight. There is also a symmetric elevation scan around boresight of +/-25° attained without the use of phase shifters. The antenna element consists of a multilayer series aperture coupled antenna fed by a meander line. It offers good performance in terms of beamscanning cost as the phase shifter requirement is reduced, owing to the use of frequency scanning in elevation and phase scanning only in the azimuth, drastically reducing the cost. The phased array has a good total efficiency around 80%. The antenna has been designed and its performance validated by full wave simulations in the solver CST Studio Suite.

Two approaches of multistage gradient robustification for image contour detection are presented in this paper: two stages of Difference of Estimates and Difference of Estimate followed by an optimal filtering. Watershed transformation is then applied to these robusti ed gradient images to effectively detect image contours which are guaranteed to be in closed form. Multistage gradient robustification provides the flexibility of using different image processing techniques and produces good detection results for the images highly corrupted with noise.

The problem of estimating propagation delays of the orthogonal modulated signals in asynchronous DS-CDMA system over fading channels is treated in this paper. The system under study resembles the uplink of an IS-95 system. The channel is assumed to be a time-varing flat Rayleigh-fading channel. The shortcoming of the sliding correlator as the standard method of code acquisition is examined and three robustified versions of delay estimators,namely whitened sliding correlator, subspace based delay estimator and MMSE based delay estimator are proposed to combat the multiple access interference (MAI) in the Rayleigh-fading channels. The performance of these estimators are compared with the computer simulations as a function of different parameters, e.g., the number of pilots, near-far resistance, signal to noise ratio,etc..

Conventional approaches of digital modulation schemes make use of amplitude, frequency and/or phase as modulation characteristic to transmit data. In this paper, we exploit circular polarization (CP) of the propagating electromagnetic carrier as modulation attribute which is a novel concept in digital communications. The requirement of antenna alignment to maximize received power is eliminated for CP signals and these are not affected by linearly polarized jamming signals. The work presents the concept of Circular Polarization Modulation for 2, 4 and 8 states of carrier and refers them as binary circular polarization modulation (BCPM), quaternary circular polarization modulation (QCPM) and 8-state circular polarization modulation (8CPM) respectively. Issues of modulation, demodulation, 3D symbol constellations and 3D propagating waveforms for the proposed modulation schemes are presented and analyzed in the presence of channel effects, and they are shown to have the same bit error performance in the presence of AWGN compared with conventional schemes while provide 3dB gain in the flat Rayleigh fading channel. © 2012 IEEE.

Extremely diverse service requirements are one of the critical challenges for the upcoming fifth-generation (5G) radio access technologies. As a solution, mixed numerologies transmission is proposed as a new radio air interface by assigning different numerologies to different subbands. However, coexistence of multiple numerologies induces the inter-numerology interference (INI), which deteriorates the system performance. In this paper, a theoretical model for INI is established for windowed orthogonal frequency division multiplexing (W-OFDM) systems. The analytical expression of the INI power is derived as a function of the channel frequency response of interfering subcarrier, the spectral distance separating the aggressor and the victim subcarrier, and the overlapping windows generated by the interferer’s transmitter windows and the victim’s receiver window. Based on the derived INI power expression, a novel INI cancellation scheme is proposed by dividing the INI into a dominant deterministic part and an equivalent noise part. A soft-output ordered successive interference cancellation (OSIC) algorithm is proposed to cancel the dominant interference, and the residual interference power is utilized as effective noise variance for the calculation of loglikelihood ratios (LLRs) for bits. Numerical analysis shows that the INI theoretical model matches the simulated results, and the proposed interference cancellation algorithm effectively mitigates the INI and outperforms the state-of-the-art W-OFDM receiver algorithms.

The effect of vehicle's proximity on the radiation pattern when the RADAR's antenna is mounted on the body of autonomous cars is analysed. Two directional radiation patterns with different specifications are placed at different locations of a realistic car body model. The simulation is performed based on ray-tracing method at 77 GHz, the standard frequency for selfdriving applications. It is shown that to obtain a robust RADAR sensor, the antenna radiation pattern is better to have relatively higher gain and lower side-lobe-level (SLL), than narrower half-power-beamwidth (HPBW) and higher front-to-back (F/B) ratio. Both academia and industry can benefit from this study.

A novel Multiple-Input and Multiple- Output (MIMO) transmission scheme termed as Generalized Quadrature Spatial Modulation (G-QSM) is proposed. It amalgamates the concept of Quadrature Spatial Modulation (QSM) and spatial multiplexing for the sake of achieving a high throughput, despite relying on low number of Radio Frequency (RF) chains. In the proposed G-QSM scheme, the conventional constellation points of the spatial multiplexing structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by the classic Amplitude/Phase Modulated (APM) constellation points. The upper bounds of the Average Bit Error Probability (ABEP) of the proposed G-QSM system in high throughput massive MIMO configurations are derived. Furthermore, an Efficient Multipath Orthogonal Matching Pursuit (EMOMP) based Compressive Sensing (CS) detector is developed for our proposed G-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes in massive MIMO configurations.

A low complexity transmit diversity scheme is derived in this paper in order to overcome the prohibitive complexity imposed by the maximum likelihood detection for the systems with space-time block code (STBC) over frequency selective channels. By taking advantage of multipath propagation and exploiting temporal diversity gain, the proposed turbo equalization algorithm significantly improves the system performance compared to the original Alamouti algorithm as well as the conventional minimum mean square error (MMSE) detection scheme.

Recently, terahertz (THz) communication has drawn considerable attention as one of the promising technologies for the future wireless communications owning to its ultra-wide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input multiple-output (MIMO) represent two effective solutions for compensating the large pathloss in THz systems. In this paper, we consider an IRS-aided multi-user THz MIMO system with orthogonal frequency division multiple access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station and reflection matrix at the IRS. {Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed.} Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated non-convex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

A novel high-isolation, monostatic, circularly polarized (CP) simultaneous transmit and receive (STAR) anisotropic dielectric resonator antenna (DRA) is presented. The Proposed antenna is composed of two identical but orthogonally positioned annular sectoral anisotropic dielectric resonators. Each circularly polarized (CP) resonator consists of alternating stacked dielectric layers of relative permittivities of 2 and 15 and is excited by a coaxial probe from the two opposite ends to have left and right-hand CP. Proper element spacing and a square absorber are placed between the resonators to maximize Tx/Rx isolation. Such a structure provides an in-band full-duplex (IBFD) CP-DRA system. Measurement results exhibit high Tx/Rx isolation better than 50 dB over the desired operating bandwidth (5.87 to 5.97 GHz) with a peak gain of 5.49 and 5.08 dBic for Ports 1 and 2, respectively.

In this paper, selection criteria of Forward Error Correction (FEC) codes, in particular, the convolutional codes are evaluated for a novel air interface scheme, called Low Density Signature Orthogonal Frequency Division Multiple Access (LDS-OFDM). In this regard, the mutual information transfer characteristics of turbo Multiuser Detector (MUD) are investigated using Extrinsic Information Transfer (EXIT) charts. LDS-OFDM uses Low Density Signature structure for spreading the data symbols in frequency domain. This technique benefits from frequency diversity in addition to its ability of supporting parallel data streams more than the number of subcarriers (overloaded condition). The turbo MUD couples the data symbols’ detector of LDS scheme with users’ FEC decoders through the message passing principle. Index Terms — Low density signature, Multiuser detection, Iterative decoding.

—Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals to guide the generation process. This enables semantic communications at extremely low data rates in future wireless networks. In this paper, we develop a latency-aware semantic communications framework with pre-trained generative models. The transmitter performs multi-modal semantic decomposition on the input signal and transmits each semantic stream with the appropriate coding and communication schemes based on the intent. For the prompt, we adopt a re-transmission-based scheme to ensure reliable transmission, and for the other semantic modalities we use an adaptive modulation/coding scheme to achieve robustness to the changing wireless channel. Furthermore , we design a semantic and latency-aware scheme to allocate transmission power to different semantic modalities based on their importance subjected to semantic quality constraints. At the receiver, a pre-trained generative model synthesizes a high fidelity signal using the received multi-stream semantics. Simulation results demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications.

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

A spectrally efficient equalization scheme with multilevel modulation is proposed in this paper for combating intersymbol interference caused by the broadband fixed wireless access (BFWA) channel. The complexity of the existing MMSE equalization schemes increases drastically with the channel memory, which make them impractical to implement when signal transmission rate goes beyond a certain limit. The proposed low complexity scheme enables the system to operate at very high data rate, and its complexity does not increase with data rate, which makes it suitable for high data rate BFWA applications.

—Terahertz (THz) and intelligent reflecting surface (IRS) have been regarded as two promising technologies to improve the capacity and coverage for future 6G networks. Generally, IRS is usually equipped with large-scale elements when implemented at THz frequency. In this case, the near-field model and beam squint should be considered. Therefore, in this paper, we investigate the far-field and near-field beam squint problems in THz IRS communications for the first time. The far-field and near-field channel models are constructed based on the different electromagnetic radiation characteristics. Next, we first analyze the far-field beam squint and its effect for the beam gain based on the cascaded base station (BS)-IRS-user channel model, and then the near-field case is studied. To overcome the far-field and near-field beam squint effects, we propose to apply delay adjustable metasurface (DAM) to IRS, and develop a scheme of optimizing the reflecting phase shifts and time delays of IRS elements, which effectively eliminates the beam gain loss caused by beam squint. Finally, simulations are conducted to demonstrate the effectiveness of our proposed schemes in combating the far-field and near-field beam squint. Index Terms—Terahertz, intelligent reflecting surface, beam squint, delay adjustable metasurface.

Covert communication provides high-level security for protecting users’privacy information. In this paper, we analyze the joint impact of an external jammer and channel uncertainty on covert communication in multi-user cognitive radio networks. Meanwhile, to fairly schedule the covert communication over multi-user cognitive radio networks, we propose a fairness secondary user (SU) scheduling scheme, which enables each SU to have the same probability for sending information covertly with the aid of an external jammer. Then, the closed-form expression for the covert rate of the scheduled SU can be obtained. Our results show that the minimal detection error probability and covert rate of the scheduled SU can be significantly improved by exploiting the channel uncertainty and random variation of interference power. Moreover, the impact of interference power on the probability of detection error and the covert rate is noticeable when channel uncertainty is large.

Time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems will play a crucial role in the deployment of emerging mobile networks in 5G and beyond. Such systems heavily rely on the reciprocity-based channel estimation for its scalability. However, the imperfect channel reciprocity, mainly caused by radio-frequency mismatches among the base station antennas, can contaminate the estimate of the effective channel response thus become a performance-limiting factor. In practice, self-calibration schemes are often applied to compensate for this type of imperfections. This work investigates two self-calibration schemes, namely relative calibration and inverse calibration. Considering a TDD massive multi-user MIMO system in the presence of both channel reciprocity error and imperfect channel estimation, we derive closed-form expressions for the receive mean-square error and provide an in-depth comparative analysis of the post-equalisation performance of two calibration schemes. The proposed analytical results are verified via Monte-Carlo simulations.

As an advanced non-orthogonal multiple access (NOMA) technique, the low density signature (LDS) has never been used in filter bank multicarrier (FBMC) systems. In this paper, we model a low density weight matrix (LDWM) to utilize the intrinsic interference in FBMC systems when single-tap equalization is employed, and propose a LDS-FBMC scheme which applies LDS to FBMC signals. In addition, a joint sparse graph for FBMC named JSG-FBMC is proposed to combine single graphs of LDS, LDWM and low density parity-check (LDPC) codes which respectively represent techniques of NOMA, multicarrier modulation and channel coding. By employing the message passing algorithm (MPA), a joint receiver performing detection and decoding simultaneously on the joint sparse graph is designed. Extrinsic information transfer (EXIT) charts and construction guidelines of the joint sparse graph are studied. Simulations show the superiority of JSG-FBMC to state-of-theart techniques such as OFDM, FBMC, LDS-OFDM, LDS-FBMC and turbo structured LDS-FBMC.

In this paper, we apply the Jacobi iterative algorithm to combat intersymbol interference caused by frequency selective channels. An analytical bound of the proposed equalizer is analyzed in order to gain an insight into its asymptotic performance. Due to the error propagation problem, the potential of this algorithm is not reached in an uncoded system. However, its extension to a coded system with the application of the turbo processing principle results in a new turbo equalization algorithm which demonstrates comparable performance with reduced complexity compared to some existing filter based turbo equalization schemes.

Non-orthogonal multiple-access (NOMA) and simultaneous wireless information and power transfer (SWIPT) are promising techniques to improve spectral efficiency and energy efficiency. However, the security of NOMA SWIPT systems has not received much attention in the literature. In this paper, an artificial noise-aided beamforming design problem is studied to enhance the security of a multiple-input single-output NOMA SWIPT system where a practical non-linear energy harvesting model is adopted. The problem is non-convex and challenging to solve. Two algorithms are proposed to tackle this problem based on semidefinite relaxation (SDR) and successive convex approximation. Simulation results show that a performance gain can be obtained by using NOMA compared to the conventional orthogonal multiple access. It is also shown that the performance of the algorithm using a cost function is better than the algorithm using SDR at the cost of a higher computation complexity.

The optimized weight spectrum sequence (OWSS) is utilized as a design criterion in this paper to determine the periodic puncturing pattern and the non-periodic puncturing pattern of partially systematic RCPT (PS-RCPT) codes for LTE systems. It is shown that the OWSS-criterion based PS-RCPT codes outperform the pseudo-random puncturing (PRP) based PS-RCPT codes. Meanwhile, it is unveiled that, the puncturing ratio of information bits should be carefully determined in PS-RCPT codes generation to achieve reasonable tradeoff between the waterfall region and the error floor region performance. © 2012 IEEE.

This paper proposes a novel spatial-modulated multicarrier sparse code-division multiple access (SM/MC-SCDMA) system for achieving massive connectivity in device-centric wireless communications. In our SM/MC-SCDMA system, the advantages of both MC signalling and SM are amalgamated to conceive a low-complexity transceiver. Sparse frequency-domain spreading is utilized to mitigate the peak-to-average power ratio (PAPR) of MC signalling, as well as to facilitate low-complexity detection using the message passing algorithm. We then analyze the single-user bit error rate performance of SM/MC-SCDMA systems communicating over frequency-selective fading channels. Furthermore, the performance of SM/MC-SCDMA systems is evaluated based on both Monte-Carlo simulations and analytical results. We demonstrate that our low-complexity SM/MCSCDMA transceivers are capable of achieving near-maximum likelihood (ML) performance even when the normalized userload is as high as two, hence constituting a variable solution to support massive connectivity in device-centric wireless systems.

In this paper, we first analyse bit error rate (BER) bounds of the distributed network coding (DNC) scheme based on the Luby-transform (LT) codes, which is a class of fountain codes, for wireless sensor networks (WSNs). Then we investigate the effect from two parameters of the degree distributions, i.e., the degree value and the proportion of odd degree, to the performance of the LT-based DNC scheme. Based on the analysis and investigation results, a degree distribution design criteria is proposed for the DNC scheme based on fountain codes over Rayleigh fading channels. We compare the performance of the DNC scheme based on fountain codes using degree distributions designed in this paper with other schemes given in the literature. The comparison results show that the degree distributions designed by using the proposed criteria have better performance.

An intelligent authentication and key agreement mechanism for e-Hospital applications is proposed in this book. In addition, a mathematical model for calculating the coverage fraction in wireless sensor networks (WSNs) is addressed.

In this paper, a compact, highly integrated multiplexing filtering antenna operating at 4.7/5.2/6.0/6.6 GHz is proposed for the first time. Different from traditional antennas, the proposed antenna has one shared radiator but four ports working in different frequency bands and thus, it can simultaneously support four different transmission channels. The proposed multiplexing antenna is composed of a patch with a U-shaped slot, two substrate integrated waveguide (SIW) cavities, and four resonator-based frequency-selective paths. The resonator-based paths can not only enhance the inter-channel isolations but also improve the impedance bandwidth. The design principles and the methods of controlling the four operating bands are studied. Measurement results agree reasonably well with the simulations, showing four channels from 4.5 to 4.8 GHz, 5.1 to 5.3 GHz, 5.85 to 6.3 GHz, and 6.4 to 6.6 GHz, respectively. The antenna also exhibits a high isolation of over 25 dB between the channels. In addition, the proposed antenna has a consistent broadside radiation pattern and polarization in the four bands, manifesting the proposed multiplexing filtering antenna can be a promising candidate for multi-service wireless communication systems.

In this paper, we focus on buffer-aided wireless powered Internet of Things (IoTs) comprising of one wireless access point (AP) and multiple devices, where the AP provides energy to all devices via downlink radio frequency (RF) energy beams. All devices utilize the harvested energy to transmit their data to the AP in a time-division multiple access (TDMA) manner. Every device is assumed to be provisioned with energy storage and data buffer to store the collected energy from the AP and its data, respectively. The problem of minimizing the long-term average age of information (AoI) of the system is formulated in this paper. By solving the problem under the Lyapunov optimization framework, the AoI-aware adaptive transmission scheme is obtained, in which downlink RF energy beamforming, downlink energy transfer and uplink access, as well as transmit power and transmission rate by every device, will be jointly adjusted in order to minimize average weightede AoI according to the underlying channel state information (CSI), the buffer state information (BSI), the energy-consumption status information (ESI) of all terminals, as well as the AoI status information (ASI). Our analysis unveils that, the status update rate at devices has a significant impact on the achievable AoI performance, and the minimum average weighted AoI can only be realized at a reasonable status update rate, which is neither too high nor too low. Moreover, flexible AoI-aware scheme can be realized by adjusting either the AoI priority level or the AoI weighting coefficient.

—The synergistic amalgamation of sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) technologies can be exploited for improving spectral efficiency and providing enhanced wireless services to massive users. In this case, however, channel estimation is a burning issue with the increasing number of users and/or antennas. To tackle this problem, we propose a novel non-coherent transmission scheme for SCMA, referred to as NC-SCMA. In the proposed NC-SCMA, each user first maps its binary data to sparse codewords, and then perform differential modulation on the non-zero dimensions. Upon receiving all users' signals, we leverage the channel hardening effect to carry out differential demodulation and multiuser detection without any instantaneous channel state information. In addition, the design of the sparse codebooks in the NC-SCMA system is investigated with the aid of the pair-wise probability. Numerical results demonstrate the superiority of the proposed technique over the benchmark scheme in terms of bit error rate performance.

The present disclosure is related to a transmitter, a receiver, a method for transmitting information, and a method of receiving information in a system which simplifies receiver structure and improves the performance in an orthogonal frequency-division multiplexing (OFDM) system. The transmitter comprises a module for generating a passive phase conjugation probe signal for transmission. The receiver comprises a passive phase conjugation module. The method for transmitting information comprises generating a passive phase conjugation probe signal for transmission. The method for receiving information comprises extracting OFDM symbols from a received signal using a method which comprises performing passive phase conjugation on the received signal.

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for openloop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBCSM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations. Index Terms—Spatial Modulation (SM), Space Time Block Coding (STBC), High throughput, Diversity gain.

In this paper we give first account of a simple analysis tool for modeling temporal compression for automatic mitigation of multipath induced intersymbol interference through the use of active phase conjugation (APC) technique. The temporal compression characteristics of an APC system is analyzed using a simple discrete channel model, and numerical results are provided to justify the theoretical findings. © 2011 EurAAP.

A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized SRDSMscheme,whichischaracterizedbyourtheoreticalanalysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker’s complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.

—This letter reports a reconfigurable intelligent surface (RIS) design, development and implementation in a millimeter-waves 5G-NR indoor system operating at 24.5 GHz. The proposed RIS is composed of 1024 elements controlled by varactor diodes in a column-wise fashion for producing linear phase gradients. Beam steering based on reflection patterns from 30º to 60º is experimentally demonstrated. Moreover, an indoor experiment is reported using a 64-QAM 5G NR signal at 24.5 GHz with bandwidth of up to 400 MHz, achieving a maximum throughput of 1.2 Gbit/s. The RIS deployment results in a channel power and signal-to-noise ratio (SNR) improvement of 21.5 and 23.8 dB, respectively, implying a significant enhancement of the measured root mean square error vector magnitude (EVMRMS) from 92.23 to 6.12 % that underscores the impact of the RIS beam steering function at the targeted angle. Index Terms—5G, 6G, beam steering, millimeter wave, reconfigurable intelligent surface.

In this paper, we consider the radio resource allocation problem for uplink OFDMA system. The existing algorithms have been derived under the assumption of Gaussian inputs due to its closed-form expression of mutual information. For the sake of practicality, we consider the system with Finite Symbol Alphabet (FSA) inputs, and solve the problem by capitalizing on the recently revealed relationship between mutual information and Minimum Mean-Square Error (MMSE). We first relax the problem to formulate it as a convex optimization problem, then we derive the optimal solution via decomposition methods. The optimal solution serves as an upper bound on the system performance. Due to the complexity of the optimal solution, a low-complexity suboptimal algorithm is proposed. Numerical results show that the presented suboptimal algorithm can achieve performance very close to the optimal solution and outperforms the existing suboptimal algorithms. Furthermore, using our proposed algorithm, significant power saving can be achieved in comparison to the case when Gaussian input is assumed.

The robustness of different interactive schemes for demodulating M-ary orthogonal signaling formats in asynchronous DS-CDMA systems to the synchronization errors is addressed. The system under study resembles the uplink of an IS-95 system. The channel is assumed to be a time-varying flat Rayleigh-fading channel. Our simulation results show that the performance degradation for the considered multi-user detectors increase linearly with synchronization errors and eventually converge to that of conventional matched filter. In order to see the impact of channel estimation on the performance of multi-user detectors, we made some comparisons between non-coherent and coherent variants of the detection algorithms

The system under study is a convolutionally coded M-ary orthogonal DS-CDMA system in time-varying frequency selective Rayleigh fading channels. With emphasis on the development of several soft demodulation algorithms, we propose an iterative multi-function process integrating demodulation,decoding and multiuser detection in this paper. The performance of the proposed algorithms are evaluated numerically and proved to achieve substantial performance gain compared to the conventional demodulation and decoding scheme, especially when the soft demodulator is assisted by interference cancellation or suppression techniques.

Broadband Fixed Wireless Access (BFWA) is quickly emerging as a strong network access alternative for the delivery of voice, data, Internet, video and multimedia type applications to business and residential customers. However,the physical limitations of the wireless channel present a fundamental technical challenge to system capacity and reliable communications. Previous studies have shown that BFWA channels are dispersive, they introduce intersymbol interference (ISI) to the transmitted signals, which greatly deteriorates the system performance. An equalization algorithm based on the algebra matrix is introduced and theoretically analyzed in this paper. The results show that this algorithm exhibits a good potential to combat ISI under certain conditions, which suggests the solutions for the future BFWA systems.

In this paper, we investigate a cellular-connected unmanned aerial vehicle (UAV) network, where multiple UAVs receive messages from base stations (BSs) in the down-link, and in the meantime, BSs serve their paired ground user equipments (UEs). To effectively manage inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design is formulated to minimize UAV's ergodic outage duration (EOD). To cope with conventional optimization techniques' inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is proposed, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in the discrete action domain and beamforming design in the continuous action regime, respectively. Numerical results illustrate the effectiveness of the proposed hybrid D3QN-TD3 algorithm, compared to representative baselines.

In this paper, a novel forgetting factor aided rectangular differential (RD) orthogonal frequency division multiplexing (OFDM) with index modulation (IM) is proposed for dispersive channels, which amalgamates the concept of RD coding for exploiting the benefits of OFDM-IM with the absence of channel state information (CSI). To be more specific, N subcarriers are partitioned into G subblocks for IM mapping. By employing RD coding during two adjacent subblocks, noncoherent detection without CSI can be carried out at the receiver. To further improve the performance, a novel forgetting factor is exploited at the receiver, which can be optimized via a closed form without extra complexity. Based on the derived forgetting factor, the upper bound of the average bit error probability (ABEP) of the proposed RD-OFDM-IM scheme is derived and is validated by the simulation results. Both the theoretical and simulation results indicate that the proposed RD-OFDM-IM scheme is capable of providing a considerable performance gain over its conventional differential OFDM (D-OFDM) counterpart.

In this paper, we tackle the problem of theoretical evaluation for the multistage parallel interference cancellation (PIC) scheme in a direct-sequence code division multiple access (DS-CDMA) system with orthogonal modulation and long scrambling codes. The studied system operates on the reverse link in a time varying multipath Rayleigh fading channel. By applying the Central Limit Theorem and some other approximations to multiple access interference (MAI) and intersymbol interference (ISI), as well as assuming identically distributed chips from a single interferer, the bit error rate (BER) performance of the PIC scheme at any stage can be recursively computed from the signal-to-noise ratio, number of users, the number of path per user, processing gain of the CDMA system, and the average received power of each path. For completeness, the BER expression is derived for chip synchronous and chip asynchronous systems over both equal and unequal power multipath channels. The proposed analysis is validated by the Monte Carlo simulations and proved to be reasonably accurate, and it gives insight into the performance and capacity one can expect from PIC-based receivers under different situations. For instance, the analytical results can be used to examine the convergence property, multipath diversity gains, and near-far resistance of the PIC scheme.

—In this paper, we propose an intelligent reflecting surface (IRS) enabled wireless powered caching system. In the proposed IRS model, a power station (PS) provides wireless energy to multiple Internet of Things (IoT) devices, delivering their information to an access point (AP) by utilizing the harvested power. The AP, equipped with a local cache, stores the IoT data to avoid waking up the IoT devices frequently. Meanwhile, we deploy the IRS involving in the wireless energy and information transfer process for performance enhancements. In this practical system, the PS and the AP could belong to different service providers. Also, the AP requires to incentivize the PS to offer a provisional energy service. We model the interaction between the PS and the AP as a Stackelberg game that jointly optimizes the transmit power of the PS, the energy price, the phase shifts of the wireless energy transfer (WET) and wireless information transfer (WIT) phases, as well as wireless caching strategies of the AP. In this way, we first derive the optimal solutions of the phase shifts and the transmit power of the PS in closed-form. We propose an alternating optimization (AO) algorithm to optimize the wireless caching strategies and the energy price iteratively. Finally, we present various numerical evaluations to validate the beneficial role of the IRS and the wireless caching strategies and the performance of the proposed scheme compared with the existing benchmark schemes.

Recently, full rate and full diversity two-group (2 Gp) and four-group (4 Gp) decodable space-time block codes (STBC) derived from quasi-orthogonal STBC (QSTBC) and designed under diversity product maximization criterion have been proposed. In this paper, we derive an upper bound of diversity product for those STBCs and discover that the diversity product of the current 2 Gp-QSTBC and 4 Gp-QSTBC has the potential to approach the upper bound for 8 transmit antennas. To this end, we propose an improved design of 2 Gp and 4 Gp STBC with increased diversity product for 8 transmit antennas by allowing sufficient number of dimensions for constellation rotation. The diversity product of the proposed two-group decodable STBC achieves the derived upper bound.

—In this paper, a reflecting metasurface is proposed to control the reflection angle by manipulating the chemical potential (CP) of graphene. The surface can operate in three anomalous reflection modes for θ = 45 • , 60 • and 75 • while it is illuminated with a normal incident electromagnetic wave (EMW). Moreover, by tuning the chemical potential of graphene sheets the proposed surface can switch off the reflection mode of operation by absorbing the incident EM power.

A Ka-band inset-fed microstrip patches linear antenna array is presented for the fifth generation (5G) applications in different countries. The bandwidth is enhanced by stacking parasitic patches on top of each inset-fed patch. The array employs 16 elements in an H-plane new configuration. The radiating patches and their feed lines are arranged in an alternating out-of-phase 180-degree rotating sequence to decrease the mutual coupling and improve the radiation pattern symmetry. A (24.4%) measured bandwidth (24.35 to 31.13 GHz)is achieved with -15 dB reflection coefficients and 20 dB mutual coupling between the elements. With uniform amplitude distribution, a maximum broadside gain of 19.88 dBi is achieved. Scanning the main beam to 49.5◦ from the broadside achieved 18.7 dBi gain with -12.1 dB sidelobe level (SLL). These characteristics are in good agreement with the simulations, rendering the antenna to be a good candidate for 5G applications.

Here, the Jacobi iterative algorithm is applied to combat intersymbol interference (ISI) caused by frequency-selective channels. The performance bound of the equaliser is analysed in order to gain an insight into its asymptotic behaviour. Because of the error propagation problem, the potential of this algorithm is not reached in an uncoded system. However, its extension to a coded system with the application of the turbo-processing principle results in a new turbo equalisation algorithm, which demonstrates comparable performance with reduced complexity compared with some existing filter-based turbo equalisation schemes; and superior performance compared with some frequency domain solutions, such as orthogonal frequency division multiplexing and single-carrier frequency domain equalisation.

In this paper, we propose a joint complex diversity coding (CDC) and channel coding based space-time-frequency codes (STFCs) to increase diversity gains over space, time and frequency. Both non-iterative and iterative decoding of joint channel coding and 3-dimensional CDC transmission are investigated. The simulation results show that the minimum mean square error (MMSE) based iterative soft decoding achieves the performance of the soft sphere decoding (SD) with reduced complexity.

—The sixth-generation (6G) has defined ultra-massive machine type communication (umMTC) as a key scenario to address advances in Internet of Things technologies. However, the dramatic increase of machine-type communication devices (MTDs)in umMTC calls for the research of efficient random access (RA) schemes. To cope with the problem of high collision rates in dense networks, a multi-dimensional RA scheme is proposed in this paper, which utilizes both power and code domain non-orthogonal multiple access (NOMA) to significantly expand the contention space. In particular, we present a novel dual decoupled Q-Learning algorithm to enable each MTD to find its unique multi-dimensional resources for transmission. Unlike the traditional Q-Learning algorithm, the proposed approach utilizes two independent Q-Learning mechanisms to reduce the scale of action sets, and make use of a novel reward function to solve the coupling relationship among multiple action sets. Simulation results indicate that the proposed scheme can notably enhance the throughput performance while effectively reducing the convergence time by compared to the existing ones.

To fully reap the benefits of massive multiple-input multiple-output hybrid analog and digital precoding in frequency division duplexing single-cell systems, a two-stage precoder is developed utilizing the signal-to-leakage-plus-noise ratio metric. The main idea of this technique is to jointly design the analog precoder based only on the long-term channel statistics information at the transmitter, i.e., the channel mean and reconstructed reduced rank covariance statistics, while the digital precoder is designed based on the instantaneous channel state information of the reduced dimensionality effective reconstructed channel. Consequently, we can significantly reduce the downlink training and uplink feedback overhead analogously to the rank of the resultant effective channel. The two extremes of full channel state information at the transmitter (CSIT) and statistical CSIT are also investigated. The performance gap between the full and statistical CSIT corroborates the importance of the proposed two-stage CSIT approach. These precoders are then extended to multi-cell systems. It is shown that the digital baseband precoder design problem reduces to the generalized Rayleigh quotient problem, while the analog precoder design problem reduces to the quotient trace problem, also known as the ratio trace problem. These dimensionality reduction problems are solved via the generalized eigenvalue decomposition method. Finally, in the presence of multiuser diversity where only a subset of the users are scheduled, to considerably alleviate the channel estimation and feedback overhead burden, a low-complexity one-stage and two-stage CSIT joint user scheduler and precoder algorithms are developed.

In this paper, a compact, broadband, planar array antenna with omnidirectional radiation in horizontal plane is proposed for the 26 GHz fifth-generation (5G) broadcast applications. The antenna element is composed of two dipoles and a substrate integrated cavity (SIC) as the power splitter. The two dipoles are placed side-by-side at both sides of the SIC and they are compensated with each other to form an omni-directional pattern in horizontal plane. By properly combing the resonant frequencies of the dipoles and the SIC, a wide impedance bandwidth from 24 to 29.5 GHz is achieved. To realize a large array while reducing the complexity, loss and size of the feeding network, a novel dual-port structure combined with radiation and power splitting functions is proposed for the 1st time. The amplitude and phase on each element of the array can be tuned, and therefore, the grating lobes level can be significantly reduced. Based on the dual-port structure, an 8-element array with an enhanced gain of over 12 dBi is designed and prototyped. The proposed antenna also features low profile, low weight and low cost, which is desirable for 5G commercial applications. Measured results agree well with the simulations, showing that the proposed high-gain array antenna has a broad bandwidth, omni-directional pattern in horizontal plane, and low side-lobes.

A novel mathematical approach is proposed for the placement of unit-cells at digital metasurfaces with applications in holography, imaging, coverage improvement and antenna array. As a proof of concept, the idea is applied to a simple unit-cell capable of providing 180 degrees of phase difference between two states of it. Used unit-cell is working at 90GHz although the idea can be used in any other frequencies. It has been shown that the proposed method is capable of providing better results compare to conventional techniques in terms of concentration of intensity and quality of produced field pattern.

Most of the existing research on degrees-of-freedom (DoF) with imperfect channel state information at the transmitter (CSIT) assume the messages are private, which may not reflect reality as the two receivers can request the same content. To overcome this limitation, we consider hybrid private and common messages. We characterize the optimal DoF region for the two-user multiple-input multiple-output (MIMO) broadcast channel with hybrid messages and imperfect CSIT. We establish a three-step procedure for the DoF converse to exploit the utmost possible relaxation. For the DoF achievability, since the DoF region has a specific three-dimensional structure w.r.t. antenna configurations and CSIT qualities, by dividing CSIT qualities into cases, we check the existence of corner point solutions, and then design a hybrid messages-aware rate-splitting scheme to achieve them. Besides, we show that to achieve the strictly positive corner points, it is unnecessary to split the private messages into unicast and multicast parts because the allocated power for the multicast part should be zero. This implies that adding a common message can mitigate the rate-splitting complexity of private messages.

This paper investigates the design of unequal error protection (UEP) codebooks for sparse code multiple access (SCMA) systems. We propose a joint LDPC code and SCMA codebook design approach by incorporating cloud-partitioning of codewords in the design of SCMA codebooks with different protection levels. The protection levels of the SCMA codebooks could be optimized based on the existing error correction code. Simulation results show that significant gains could be obtained using code-aware UEP SCMA codebooks compared to codebooks designed independently of the channel code.

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. First, cluster heads are selected for each beam, and analog precoding based on discrete phase is designed. Then, users are clustered based on channel correlation, and NOMA technology is employed to serve the users. In addition, a low-complexity forced-zero method is utilized to design digital precoding in order to eliminate inter-cluster interference. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS subject to the total transmit power budget, minimal achievable rate requirement of each user, and IRS reflection coefficients. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results verify the convergence of the proposed algorithm, and deploying IRS can bring significant beamforming gains to suppress the eavesdropping.

In this work, we investigate a novel simultaneous transmission and reflection reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output downlink system, where three practical transmission protocols, namely, energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. For the system under consideration, we maximize the weighted sum rate with multiple coupled variables. To solve this optimization problem, a block coordinate descent algorithm is proposed to reformulate this problem and design the precoding matrices and the transmitting and reflecting coefficients (TARCs) in an alternate manner. Specifically, for the ES scheme, the precoding matrices are solved using the Lagrange dual method, while the TARCs are obtained using the penalty concave-convex method. Additionally, the proposed method is extended to the MS scheme by solving a mixed-integer problem. Moreover, we solve the formulated problem for the TS scheme using a one-dimensional search and the Majorization-Minimization technique. Our simulation results reveal that: 1) Simultaneous transmission and reflection RIS (STAR-RIS) can achieve better performance than reflecting-only RIS; 2) In unicast communication, TS scheme outperforms the ES and MS schemes, while in broadcast communication, ES scheme outperforms the TS and MS schemes.

Gray mapping is a well-known way to improve the performance of regular constellation modulation , but it is challenging to be applied directly for irregular alternative. To address this issue, in this paper, a unified bit-to-symbol mapping method is designed for generalized constellation modulation (i.e., regular and irregular shaping). The objective of the proposed approach is to minimize the average bit error probability by reducing the hamming distance (HD) of symbols with larger values of pairwise error probability. Simulation results show that the conventional constellation modulation(i.e., phase shift keying and quadra-ture amplitude modulation (QAM) with the proposed mapping rule yield the same performance as that of classical gray mapping. Moreover, the recently developed golden angle modulation (GAM) with the proposed mapping method is capable of providing around 1 dB gain over the conventional mapping counterpart and offers comparable performance to QAM with Gray mapping.

To flexibly support diverse communication requirements (e.g., throughput, latency, massive connection, etc.) for the next generation wireless communications, one viable solution is to divide the system bandwidth into several service subbands, each for a different type of service. In such a multi-service (MS) system, each service has its optimal frame structure while the services are isolated by subband filtering. In this paper, a framework for multi-service (MS) system is established based on subband filtered multi-carrier (SFMC) modulation. We consider both single-rate (SR) and multi-rate (MR) signal processing as two different MS-SFMC implementations, each having different performance and computational complexity. By comparison, the SR system outperforms the MR system in terms of performance while the MR system has a significantly reduced computational complexity than the SR system. Numerical results show the effectiveness of our analysis and the proposed systems. These proposed SR and MR MS-SFMC systems provide guidelines for next generation wireless system frame structure optimization and algorithm design.

A Ka-band inset-fed microstrip patches linear antenna array is presented for the fifth generation (5G) applications in different countries. The bandwidth is enhanced by stacking parasitic patches on top of each inset-fed patch. The array employs 16 elements in an H-plane new configuration. The radiating patches and their feed lines are arranged in an alternating out-of-phase 180-degree rotating sequence to decrease the mutual coupling and improve the radiation pattern symmetry. A (24.4%) measured bandwidth (24.35 to 31.13 GHz)is achieved with -15 dB reflection coefficients and 20 dB mutual coupling between the elements. With uniform amplitude distribution, a maximum broadside gain of 19.88 dBi is achieved. Scanning the main beam to 49.5° from the broadside achieved 18.7 dBi gain with -12.1 dB sidelobe level (SLL). These characteristics are in good agreement with the simulations, rendering the antenna to be a good candidate for 5G applications.

Massive connectivity for extra large-scale multi-input multi-output (XL-MIMO) systems is a challenging issue due to the near-field access channels and the prohibitive cost. In this paper, we propose an uplink grant-free massive access scheme for XL-MIMO systems, in which a mixed-analog-to-digital converters (ADC) architecture is adopted to strike the right balance between access performance and power consumption. By exploiting the spatial-domain structured sparsity and the piecewise angular-domain cluster sparsity of massive access channels, a compressive sensing (CS)-based two-stage orthogonal approximate message passing algorithm is proposed to efficiently solve the joint activity detection and channel estimation problem. Particularly, high-precision quantized measurements are leveraged to perform accurate hyper-parameter estimation, thereby facilitating the activity detection. Moreover, we adopt a subarray-wise estimation strategy to overcome the severe angular-domain energy dispersion problem which is caused by the near-field effect in XL-MIMO channels. Simulation results verify the superiority of our proposed algorithm over state-of-the-art CS algorithms for massive access based on XL-MIMO with mixed-ADC architectures.

We derive the uplink system model for In-band and Guard-band narrowband Internet of Things (NB-IoT). The results reveal that the actual channel frequency response (CFR) is not a simple Fourier transform of the channel impulse response, due to sampling rate mismatch between the NB-IoT user and Long Term Evolution (LTE) base station. Consequently, a new channel equalization algorithm is proposed based on the derived effective CFR. In addition, the interference is derived analytically to facilitate the co-existence of NB-IoT and LTE signals. This work provides an example and guidance to support network slicing and service multiplexing in the physical layer.

Edge computing is a viable paradigm for supporting the Industrial Internet of Things deployment by shifting computationally demanding tasks from resource-constrained devices to powerful edge servers. In this study, mobile edge computing (MEC) services are provided for multiple ground mobile nodes (MNs) through a time-division multiple access protocol using the unmanned aerial vehicle (UAV)-enabled edge servers. Remotely controlled UAVs can serve as MEC servers due to their adaptability and flexibility. However, the current MEC approaches have proven ineffective in situations where the number of MNs rapidly increases, or network resources are sparsely distributed. Furthermore, suitable accessibility across wireless networks via MNs with an acceptable quality of service is a fundamental problem for conventional UAV-assisted communications. To tackle this issue, we present an optimized computation resource allocation model using cooperative evolutionary computation to solve the joint optimization problem of queuebased computation offloading and adaptive computing resource allocation. The developed method ensures the task computation delay of all MNs within a time block, optimizes the sum of MN’s accessibility rates, and reduces the energy consumption of the UAV and MNs while meeting task computation restrictions. Moreover, we propose a multilayer data flow processing system to make full use of the computational capability across the system. The top layer of the system contains the cloud centre, the middle layer contains the UAV-assisted MEC (U-MEC) servers, and the bottom layer contains the mobile devices. Our numerical analysis and simulation results prove that the proposed scheme outperforms conventional techniques such as equal offloading time allocation and straight-line flight.

—Sparse code multiple-access (SCMA) is an emerging technique to support massive connectivity in 5G networks and beyond. In SCMA transmissions, some resource-blocks may undergo certain contamination due to deep fading and/or jamming attacks, thus leading to severe performance degradation over such contaminated ones. Besides, the current SCMA infrastructure is normally deployed in single-cell. To deploy the SCMA into multi-cell networks under contaminated/jamming channels, we propose a novel frequency-hopping based SCMA (FH-SCMA) for quasi-synchronous multi-cell networks, in which the entire subcarrier-channels of every codeword keep hopping over the multiple resource-blocks according certain hopping pattern. We propose and design a pseudo-randomly orthogonal hopping pattern to adapt to the specific requirements of quasi-synchronous FH-SCMA multi-cell networks. Our analysis and simulation results indicate that the proposed FH-SCMA leads to both improved user capacity and error-rate performance, whilst remaining resilient to the inter-cell interference.

In this letter, we study the beamforming design in a lens-antenna array-based joint multicast-unicast millimeter wave massive MIMO system, where the simultaneous wireless information and power transfer at users is considered. First, we develop a beam selection scheme based on the structure of the lens-antenna array and then, the zero forcing precoding is adopted to cancel the inter-unicast interference among users. Next, we formulate a sum rate maximization problem by jointly optimizing the unicast power, multicast beamforming and power splitting ratio. Meanwhile, the maximum transmit power constraint for the base station and the minimum harvested energy for each user are imposed. By employing the successive convex approximation technique, we transform the original optimization problem into a convex one, and propose an iterative algorithm to solve it. Finally, simulation results are conducted to verify the effectiveness of the proposed schemes.

A novel equalization algorithm utilizing improper nature of the intersymbol interference (ISI) is introduced in this paper. We show that full exploitation of the available information on the second-order statistics of the observed signal entails widely linear processing and that previously known linear minimum mean square error (MMSE) equalizers represent sub-optimum solutions. The proposed scheme is generally applicable for both real and complex signal constellations. The results show that accounting for the improper nature of the ISI leads to significant performance gain compared to conventional equalization schemes.

Filter bank multicarrier systems with quadrature amplitude modulation (FBMC/QAM) have drawn attentions to get the advantage of complex symbol transmission, as well as very low out of band radiation and relaxed synchronization requirements for asynchronous scenarios. In order to make this system viable for practical deployment, the biggest challenge is designing appropriate filters to minimize the interference between adjacent subcarriers, while maintaining the Nyquist property of the filter. We show that the deviation from the Nyquist property can be compensated through the fractional shift of the filtered symbols, which provides flexibility to optimize the stopband of the filter. The proposed design method shows advantages over the state of the art designs, and provides guidance for the filter design in practical FBMC/QAM systems.

This paper investigates the amalgamation of affine frequency division multiplexing (AFDM) with sparse code multiple access (SCMA), termed as AFDM-SCMA, to facilitate massive connectivity in high-mobility scenarios. We start by introducing the basic principles of SCMA and AFDM systems and then present the proposed AFDM-SCMA system with multiple input and multiple output (MIMO) for both downlink and uplink channels. A two stage detector is proposed for the multi-user detection of the downlink channels. Additionally, to reduce the detection complexity and exploit the channel sparsity, we propose an expectation propagation algorithm (EPA)-aided low complexity receiver for uplink channels. Through numerical simulations, we validate the enhanced performance of the proposed AFDMSCMA systems compared to conventional orthogonal frequency division multiplexing-empowered SCMA (OFDM-SCMA) systems in terms of error rate performance.

Phase-shifter-aided spatial multiplexing (PSSM) is a low-complexity multiple-input multiple-output (MIMO) technique with the assistance of extra phase shifters. Following the basic structure of the PSSM transmitter, in this contribution, a message passing detector tailored for PSSM systems is developed, and is further simplified according to the paradigm of approximate message passing (AMP). Analytical and simulation results confirm that the proposed detector significantly outperforms the linear detectors and strikes a good balance between system performance and computational complexity, compared to its maximal likelihood (ML) counterpart.

With the anticipation of sixth-generation (6G) networks escalating, the integrated sensing and communication (ISAC) and millimeter-wave (mmWave) unmanned aerial vehicle (UAV) communications emerge as the key focus areas. This paper presents an innovative approach to ISAC waveform design tailored for mmWave UAV communications. The orthogonal chirp division multiplexing (OCDM), characterized by a brunch of orthogonal chirp signals, is firstly introduced in UAV scenarios to offer dual sensing and communication functionalities. We propose an holistic waveform design which incorporates OCDM with the state-of-the-art mmWave frequency-modulated continuous wave (FMCW) radar. Specifically, one subcarrier in OCDM is chosen as the dedicated sensing signal to facilitate the FMCW processing at the UAV receiver, while the rest of OCDM subcarriers can be used to enhance communication data rate. Such OCDM-FMCW scheme significantly reduces the required hardware complexity, particularly in analog-to-digital converter, which provides an energy-efficient ISAC solution for the resource-constraint UAVs. Simulation results demonstrate the effectiveness and superiority of the proposed scheme. It can surpass traditional methods like OFDM and OTFS, by trading off the sensing performance, communication performance, and hardware complexity.

The discrete cosine transform (DCT) based multicarrier modulation (MCM) system is regarded as one of the promising transmission techniques for future wireless communications. By employing cosine basis as orthogonal functions for multiplexing each real-valued symbol with symbol period of T , it is able to maintain the subcarrier orthogonality while reducing frequency spacing to 1/(2T ) Hz, which is only half of that compared to discrete Fourier transform (DFT) based multicarrier systems. In this paper, following one of the effective transmission models by which zeros are inserted as guard sequence and the DCT operation at the receiver is replaced by DFT of double length, we reformulate and evaluate three classic detection methods by appropriately processing the post- DFT signals both for single antenna and multiple-input multipleoutput (MIMO) DCT-MCM systems. In all cases, we show that with our reformulated detection approaches, DCT-MCM schemes can outperform, in terms of error-rate, conventional OFDMbased systems.

In this paper, we study the performance of the turbo equalization schemes for systems with space-time block code (STBC) using the extrinsic information transfer (EXIT) chart, which is shown to be very useful for analyzing the convergence behavior of turbo equalizers, predicting the expected BER performance of the STBC coded systems, determining the SNR threshold for a target BER, as well as facilitating the proper choice of equalizers and channel codes for specific channel conditions

In asynchronous (intermittent) interference scenarios, the content of co-channel interference sources over the data interval may be different from the interferers content over the training interval, typically with extra interference sources presented over the data interval. Under such conditions, conventional adaptive beamformer designed over the training interval may lose its efficiency when applied to the data interval. In this paper, we address the problem by 1) formulating a family of the second order statistics adaptive beamformers regularized by the covariance matrix estimated over the data interval; 2) proposing a maximum likelihood methodology for optimization of the combined (mixed) covariance matrix based on maximization of a product of a likelihood ratio that checks the accuracy of the recovered training signals and a likelihood ratio on equality of the eigenvalues in complementary to the signal subspace defined over the data interval; 3) demonstrating efficiency and robustness of the proposed solution as a linear adaptive beamformer and as an initialization for iterative beamformer with projections to the finite alphabet in different asynchronous interference scenarios comparing with the basic training and data based interference rejection combining receivers.

In this paper, we propose golden angle modulation aided orthogonal frequency division multiplexing with index modulation (OFDM-IM-GAM) for an arbitrary number of subcarriers, which includes integer OFDM-IM-GAM (I-OFDM-IMGAM) and fractional OFDM-IM-GAM (F-OFDM-IM-GAM). Furthermore, a general mapping optimization algorithm is developed for our proposed systems, where it is implemented by minimizing the hamming distance (HD) of transmission vectors with larger values of pairwise error probability (PEP) to minimize average bit error probability (ABEP). The simulation results illustrate the superiority of the I-OFDM-IM-GAM and FOFDM-IM-GAM systems with the proposed mapping optimization algorithm over the conventional counterparts in the cases of uncoded and coded systems.

Reconfigurable intelligent surface (RIS) has recently drawn substantial attention toward performance enhancement in wireless communication systems. One of the key challenges in RIS-assisted systems is to design efficient joint beamforming algorithms. However, most existing algorithms rely on instantaneous channel state information (CSI) and iterative optimization methods, which suffer from high computational complexity. Therefore, by exploiting the property of the recent popular fluid antennas, this paper proposes a low-complexity joint beamforming scheme for an RIS-assisted fluid antenna system (RIS-FAS) requiring only statistical CSI. Specifically, by carefully adjusting the FA array at the transmitter side, it is possible to respectively steer its main-lobe (ML) and grating-lobe (GL) towards the user and RIS, where the line-of-sight (LoS) channels from the transmitter to the user and RIS are guaranteed to be identical, allowing independent beamforming for the three cascade channels. Theoretical analysis and simulation results validate that the proposed scheme achieves both high-performance and low-complexity characteristics compared with its conventional counterparts.

Different detection schemes for multiple-input, multiple-output (MIMO) systems are investigated. By enhancing the interference and noise estimation, we propose a novel MIMO receiver strategy, which is shown to achieve superior performance with moderate increase in computational complexity compared to conventional MIMO detection schemes.

This paper presents an analysis on performance of an ultra dense network (UDN) with and without cell cooperation from the perspective of network information theory. We propose a UDN performance metric called Total Average Geometry Throughput which is independent from the user distribution or scheduler etc. This performance metric is analyzed in detail for UDN with and without cooperation. The numerical results from the analysis show that under the studied system model, the total average geometry throughput reaches its maximum when the inter-cell distance is around 6 ~ 8 meters, both without and with cell cooperation. Cell cooperation can significantly reduce inter-cell interference but not remove it completely. With cell cooperation and an optimum number of the cooperating cells the maximum performance gain can be achieved. Furthermore, the results also imply that there is an optimum aggregate transmission power if considering the energy cost per bit.

This study provides a general diversity analysis for joint complex diversity coding (CDC) and channel coding-based space-time-frequency codeing is provided. The mapping designs from channel coding to CDC are crucial for efficient exploitation of the diversity potential. This study provides and proves a sufficient condition of full diversity construction with joint three-dimensional CDC and channel coding, bit-interleaved coded complex diversity coding and symbol-interleaved coded complex diversity coding. Both non-iterative and iterative detections of joint channel code and CDC transmission are investigated. The proposed minimum mean-square error-based iterative soft decoding achieves the performance of the soft sphere decoding with reduced complexity.

This paper investigates power allocation for a downlink non-orthogonal multiple access (NOMA) system with a base station and two users under imperfect channel estimation, where the variance of the channel estimation error for each transmission link is assumed to be known. We first present approximated user capacity expressions according to the error variance information, and then formulate a power allocation optimization problem between the two users for maximizing the minimum approximated user capacity under a total power constraint. Since such a max-min problem is equivalent to maximizing the minimum approximated received signal-to-interference-plusnoise ratio (SINR), a closed-form power allocation solution can be derived based on SINR balancing between the two users. The proposed method involves solving a quadratic equation, and only low implementation complexity is required for practical applications. Computer simulation results show that the proposed max-min power allocation scheme is more robust against channel estimation errors and achieves better worst bit-error-rate performance for the two users, as compared with the conventional fixed power allocation approach and the related max-min method derived according to perfect channel state information.

Recently, a single-symbol decodable transmit strategy based on preprocessing at the transmitter has been introduced to decouple the quasi-orthogonal space-time block codes (QOSTBC) with reduced complexity at the receiver. Unfortunately, it does not achieve full diversity, thus suffering from significant performance loss. To tackle this problem, we propose a full diversity scheme with four transmit antennas in this letter. The proposed code is based on a class of restricted full-rank single-symbol decodable design (RFSDD) and has many similar characteristics as the coordinate interleaved orthogonal designs (CIODs), but with a lower peak-to-average ratio (PAR).

The asymptotic performance of the space-time block coded systems using two transmit antennas over a broadband wireless fixed access (FWA) frequency selective channel is studied in this paper. The conducted theoretical analysis gives us an insight into the physical limitations imposed by the FWA channels and suggests solutions to improve the capacity and performance of future FWA systems.

As 5G networks are rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for enabling massive connectivity. We propose to apply SCMA to a massively distributed access system (MDAS), whose architecture is based on fiber-based visible light communication (FVLC), ultra-dense network (UDN), and NOMA. Under this framework, we consider the interactions between optical front-hauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks.

—Multi-numerology multi-carrier (MN-MC) techniques are considered as essential enablers for RAN slicing in fifth-generation (5G) communication systems and beyond. However, utilization of mixed numerologies breaks the or-thogonality principle defined for single-numerology orthogonal frequency division multiplexing (SN-OFDM) systems with a unified subcarrier spacing. This leads to interference between different numerologies, i.e., inter-numerology interference (INI). This paper develops metrics to quantify the level of the INI using a continuous-time approach. The derived analytical expressions of INI in terms of mean square error (MSE) and error vector magnitude (EVM) directly reveal the main contributing factors to INI, which can not be shown explicitly in a matrix form INI based on discrete-time calculations. Moreover, the study of power offset between different numerologies shows a significant impact on INI, especially for high order modulation schemes. The finding in this paper provides analytical guidance in designing multi-numerology (MN) systems, for instance, developing resource allocation schemes and interference mitigation techniques.

In this paper, Generalized Space-Time Block Coded Spatial Modulation (GSTBC-SM) is proposed for Multiple-Input and Multiple-Output (MIMO) system, which can be extended into an arbitrary even number of Transmit Antennas (TAs). The proposed GSTBC-SM scheme employs the hybrid concepts of Generalized Space-Time Block Coding (GSTBC) and Spatial Modulation (SM) to further exploit the diversity benefits of GSTBC using sparse Radio Frequency (RF) chains. To be more specific, the information bits are divided into Nu groups and each group is modulated by SM scheme. Finally, the Nu symbols are invoked for GSTBC structure. In order to demonstrated the advantages of our proposed GSTBCSM schemes, the theoretical Average Bit Error Probability (ABEP) of our proposed GSTBC-SM is derived. Both our analytical and simulation results demonstrated that the proposed GSTBC-SM scheme is capable of providing considerable performance gains over the corresponding GSTBC schemes at the same transmit rate associated with the same number of RF chains.

Targeting to provide reliable short-packet communications with tens of bits in machine-type networks, we investigate a novel sparse code multiple access (SCMA) scheme called delayed bit-interleaved coded SCMA (DBIC-SCMA). At the transmitter side, a delay module is introduced between each channel encoder and SCMA mapper such that data bits from different channel codewords can be mapped to an identical SCMA codeword. We present the main components and principles for the transmitter of DBIC-SCMA, followed by a pre-decoding assisted receiver design which exploits systematic and rate-adaptive properties of certain channel codes. Our simulation results show that the proposed DBIC-SCMA leads to significant improvements in error rate performance over the classical BIC-SCMA scheme.

In this paper, we investigate the throughput performance of single-packet and multi-packet hybrid-automatic repeat request (HARQ) with blanking for downlink non-orthogonal multiple access (NOMA) systems. While conventional singlepacket HARQ achieves high throughput at the expense of high latency, multi-packet HARQ, where several data packets are sent in the same channel block, can achieve high throughput with low latency. Previous works have shown that multi-packet HARQ outperforms single-packet HARQ in orthogonal multiple access (OMA) systems, especially in the moderate to high signal-tonoise ratio regime. This work amalgamates multi-packet HARQ with NOMA to achieve higher throughput than the conventional single-packet HARQ and OMA, which has been adopted in the legacy mobile networks. We conduct theoretical analysis for the throughput per user and also investigate the optimization of the power and rate allocations of the packets, in order to maximize the weighted-sum throughput. It is demonstrated that the gain of multi-packet HARQ over the single-packet HARQ in NOMA systems is reduced compared to that obtained in OMA systems due to inter-user interference. It is also shown that NOMAHARQ cannot achieve any throughput gain with respect to OMAHARQ when the error propagation rate of the NOMA detector is above a certain threshold.

This paper investigates a two-tier heterogeneous networks (HetNets) with wireless backhaul, where millimeter wave (mmWave) frequency is adopted at the macro base station (MBS), and the cellular frequency is considered at small cell BS (SBS) with orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is investigated to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users’ quality of service (QoS) constraints. The formulated problem is non-convex mixed integer nonlinear fraction programming (MINLFP), which is non-trivial to solve directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we transform the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is considered to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal closed-form power allocation. Furthermore, we analyze the convergence of the proposed iterative algorithm. Numerical results are presented to demonstrate our proposed schemes.

This work addresses joint transceiver optimization for multiple-input, multiple-output (MIMO) systems. In practical systems the complete knowledge of channel state information (CSI) is hardly available at transmitter. To tackle this problem, we resort to the codebook approach to precoding design, where the receiver selects a precoding matrix from a finite set of predefined precoding matrices based on the instantaneous channel condition and delivers the index of the chosen precoding matrix to the transmitter via a bandwidth-constraint feedback channel. In this paper, the codebook based precoding design at transmitter is optimized jointly with decoding design at receiver. © 2011 IEEE.

We present the channel estimation algorithms for the asynchronous direct-sequence code-division multiple access (DS-CDMA) systems employing the orthogonal signalling formats and long scrambling codes. The performance of a communication system depends largely on its ability to retrieve an accurate measurement of the underlying channel. We investigated channel estimation algorithms under different conditions. The estimated channel information is used to enable the coherent data detection to combat the detrimental effect of multipath propagation of the transmitted signal as well as multiple access interference (MAI). Different channel estimation schemes are evaluated and compared in terms of mean square error (MSE) of the channel estimate and the bit error rate (BER) performance. Based on our analysis and numerical results, some recommendations are made on how to choose appropriate channel estimators in practical systems.

Broadband Fixed Wireless Access (BFWA) has attracted considerable attention as a promising approach for the next generation high quality and high speed wireless access in frastructure. However, previous studies have shown that BFWA channels are dispersive, they introduce intersym bol interference (ISI) to the transmitted signals, which greatly deteriorates the system performance. In this pa per, we show that the effect of ISI can be greatly alleviated by proper equalization and decoding design, the system performance and capacity can be significantly improved compared to the conventional equalization and decoding scheme.

—This paper investigates a joint intelligent transmis-sive surface (ITS) and intelligent reflecting surface (IRS)-assisted cell-free network. Specifically, various ITS-assisted base stations (BSs), handled via a central processing unit (CPU), broadcast information signals to multiple IoT devices, carried by active transmit beamforming and transmissive reflecting phase shifts. Meanwhile, an IRS passively reflect signals from the ITS-assisted BS to the IoT devices. To examine this network performance, a sum rate is maximized among all users to jointly optimize the active beamforming, ITS and IRS passive beampatterns. These coupled variables leads to the non-convexity of this formulated optimization problem, which cannot be directly solved. To deal with this issue, we begin with applying the Lagrange dual transformation (LDT) and quadratic transformation (QT) to recast the sum of multiple logarithmically fractional objectives to the subtractive form, and further to quadratic form. Next, an alternating optimization (AO) algorithm is presented to separately the active beamforming, ITS and IRS passive beampatterns in an iterative fashion. Each sub-optimal solution of these variables can be iteratively derived, in a closed-form, by solving the quadratic objective function with a convex constraint or a unit-modulus constraint via the dual method with bisection search or the Alternating Direction Method of Multipliers (ADMM) algorithm. Finally, simulation results are provided to confirm the performance of the proposed algorithm compared to several benchmark schemes. Index Terms—Cell-free networks, intelligent reflecting/transmissive surface (IRS/ITS), Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMM).

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for low-power wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming and uplink multi-user detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penalty-based optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

Low latency and energy efficiency are two important performance requirements in various fifth-generation (5G) wire-less networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

This letter studies high-capacity visible light communication (VLC) based on code-domain non-orthogonal multiple access (CD-NOMA) with the goal of enabling the future machine-type communication networks. To fulfill the non-negative signal constraint and mitigate/suppress the nonlinear effects and shot noise, a novel uniform-distributed constellation codebook is developed for lower peak power and larger minimum Euclidean distance (MED). The simulation results demonstrate that our proposed codebooks give rise to significantly improved error rate performance compared to existing codebooks. In addition, codebooks with larger overloading factors are presented to achieve high-capacity communication.

An index modulation (IM) assisted Discrete Cosine Transform based Orthogonal Frequency Division Multiplexing (DCT-OFDM) with Enhanced Transmitter Design (termed as EDCT-OFDM-IM) is proposed. It amalgamates the concept of Discrete Cosine Transform assisted Orthogonal Frequency Division Multiplexing (DCT-OFDM) and Index Modulation (IM) to exploit the design freedom provided by the double number of available subcarrier under the same bandwidth. In the proposed EDCT-OFDM-IM scheme, the maximum likelihood (ML) detector used for symbol bits and index bits recovering is derived and the sophisticated designing guidelines for EDCTOFDM-IM are provided. Based on the derived pairwise error event probability, a theoretical upper bound on the average biterror probability (ABEP) of EDCT-OFDM-IM is provided over multipath fading channels. Furthermore, the maximum peak-toaverage power ratio (PAPR) of our proposed EDCT-OFDM-IM scheme is derived and compared to than the general Discrete Fourier Transform (DFT) based OFDM-IM counterpart.

Discrete cosine transform (DCT) based orthogonal frequency division multiplexing (OFDM), which has double number of subcarrier compared to the classic discrete fourier transform (DFT) based OFDM (DFT-OFDM) at the same bandwidth, is a promising high spectral efficiency multicarrier techniques for future wireless communication. In this paper, an enhanced DCT-OFDM with index modulation (IM) (EDCT-OFDM-IM) is proposed to further exploit the benefits of the DCT-OFDM and IM techniques. To be more specific, a pre-filtering method based DCT-OFDM-IM transmitter is first designed and the non-linear maximum likelihood (ML) is developed for our EDCT-OFDM-IM system. Moreover, the average bit error probability (ABEP) of the proposed EDCT-OFDM-IM system is derived, which is confirmed by our simulation results. Both simulation and theoretical results are shown that the proposed EDCT-OFDM-IM system exhibits better bit error rate (BER) performance over the conventional DFT-OFDM-IM and DCT-OFDM-IM counterparts.

A device-to-device (D2D) ultra reliable low latency communications (URLLC) network is investigated in this paper. Specifically, a D2D transmitter opportunistically accesses the radio resource provided by a cellular network and directly transmits short packets to its destination. A novel performance metric is adopted for finite block-length code. We quantify the maximum achievable rate for the D2D network, subject to a probabilistic interference power constraint based on imperfect channel state information (CSI). First, we perform a convexity analysis which reveals that the finite block-length rate for the D2D pair in short-packet transmission is not always concave. To address this issue, we propose two effective resource allocation schemes using the successive convex approximation (SCA)-based iterative algorithm. To gain more insights, we exploit the mono- tonicity of the average finite block-length rate. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the maximum achievable average rate in the finite block-length regime. Numerical results are provided to confirm the effectiveness of the proposed resource allocation schemes and validate the accuracy of the derived theoretical results.

A compact size, dual-band wearable antenna for off-body communication operating at the both 2.45 and 5.8 GHz industrial, scientific, and medical (ISM) band is presented. The antenna is a printed monopole on an FR4 substrate with a modified loaded ground plane to make the antenna profile compact. Antennas’ radiation characteristics have been optimized while the proposed antenna placed close to the human forearm. The fabricated antenna operating on the forearm has been measured to verify the simulation results.

The advent of mixed-numerology multi-carrier (MN-MC) techniques adds flexibilities in supporting heterogeneous services in fifth generation (5G) communication systems and beyond. However, the coexistence of mixed numerologies destroys the orthogonality principle defined for single-numerology orthogonal frequency division multiplexing (SN-OFDM) systems with overlapping subcarriers of uniform subcarrier spacing. Consequently, the loss of orthogonality leads to inter-numerology interference (INI), which complicates signal generation and impedes signal isolation. In this paper, the INI in MN-OFDM systems is characterized through mathematical modeling and is shown to primarily rely on system parameters with regard to the pulse shape, the relative distance between subcarriers and the numerology scaling factor. Reduced-form formulas for the INI in continuous-time and discrete-time MN systems are derived. The derived mathematical framework facilitates the study of the effect of discretization on the INI and partial orthogonality existing in subsets of the subcarriers. The reduced-form formulas can also be used in developing interference metrics and designing mitigation techniques.

In the upcoming B5G/6G era, devices will generate a amount of heterogeneous data at the network edge. As a paradigm for implementing distributed and privacy-preserving machine learning (ML), Federated Learning (FL) has drawn great attention to secure data sharing in edge networks. However, FL takes too much time and communication resources to train and transmit model parameters, which is unaffordable for edge devices with limited capabilities. To achieve a trade-off between resource and efficiency, it is crucial to select appropriate training nodes. While existing works about node selection focus on the resources allocation and pay less attention to the node mobility and seamless service. In this paper, we considering mobility, computation capability, and transmission power of training nodes to minimize the FL system cost. We propose an algorithm and mechanism respectively for different scenarios of node speed. An algorithm based on Deep Reinforcement Learning (DRL) matches with stationary and low-speed training nodes. A heuristic mechanism is used for nodes with high mobility. Simulation results show that the proposed schemes select appropriate training nodes effectively, and reduce the system cost by up to 20%.

5G Non-Terrestrial Network (NTN) technology has successfully triggered the convergence across satellite and mobile cellular ecosystems. Compared with proprietary technologies, the open standard 5G NTN technology can enable satellite communications in more affordable devices and mainstream consumer markets thanks to economics of scale. The recent examples by smart phone direct access to satellite already demonstrates high market interests and potential which will benefit both ecosystems.

In the literature, the Gaussian input is assumed in power optimization algorithms. However, this assumption is unrealistic, whereas practical systems use Finite Symbol Alphabet (FSA) input, (e.g., M-QAM). In this paper, we consider the optimal power for joint interweave and underlay CR systems given FSA inputs. We formulated our problem as convex optimization and solved it through general convex optimization tools. We observed that the total SU transmit power is always less than the power budget and remains in interference limited region only over the considered distance range. Therefore, we re-derive optimal power with interference constraint only in order to reduce the complexity of the algorithm by solving it analytically. Numerical results reveal that, for the considered distance range, the transmit power saving and the rate gain with the proposed algorithm is in the range 16-92% and 7-34%, respectively, depending on the modulation scheme (i.e., BPSK, QPSK and 16-QAM) used.

In time-division-duplexing (TDD) massive multipleinput multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radiofrequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closedform expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

This paper investigates the secrecy performance of full-duplex relay (FDR) networks. The resulting analysis shows that FDR networks have better secrecy performance than half duplex relay networks, if the self-interference can be well suppressed. We also propose a full duplex jamming relay network, in which the relay node transmits jamming signals while receiving the data from the source. While the full duplex jamming scheme has the same data rate as the half duplex scheme, the secrecy performance can be significantly improved, making it an attractive scheme when the network secrecy is a primary concern. A mathematic model is developed to analyze secrecy outage probabilities for the half duplex, the full duplex and full duplex jamming schemes, and the simulation results are also presented to verify the analysis.

—In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications. Index Terms—Space-air-ground integrated networks, edge computing , resource allocation, unmanned aerial vehicle, deep reinforcement learning.

This letter proposes a hybrid beamforming design for an intelligent transmissive surface (ITS)-assisted transmitter wireless network. We aim to suppress the sidelobes and optimize the mainlobes of the transmit beams by minimizing the proposed cost function based on the least squares (LS) for the digital beamforming vector of the base station (BS) and the phase shifts of the ITS. To solve the minimization problem, we adopt an efficient algorithm based on the alternating optimization (AO) method to design the digital beamforming vector and the phase shifts of the ITS in an alternating manner. In particular, the alternating direction method of multipliers (ADMM) algorithm is utilized to obtain the optimal phase shifts of the ITS. Finally, we verify the improvement achieved by the proposed algorithm in terms of the beam response compared to the benchmark schemes by the simulation results.

In this paper, we investigate the secrecy performance for a multiple-input multiple-output (MIMO) wiretap channel in the presence of a multiantenna eavesdropper. In particular, the legitimate transmitter uses transmit antenna selection (TAS) to transmit on a single antenna with the largest signal-to-noise ratio (SNR) while both the legitimate receiver and the eavesdropper adopt maximal ratio combining (MRC) for reception. We derive exact closed-form expressions for the probabilities of achieving positive secrecy rate and secrecy outage in the case of imperfect feedback due to feedback delay and/or feedback error. Furthermore, we derive the asymptotic secrecy outage probability at high SNR, which accurately reveals the secrecy diversity loss due to imperfect feedback. Simulation results are provided to verify our analytical results and illustrate the impact of imperfect feedback on the secrecy performance of such a wiretap system.

Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper’s channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper’s channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper’s channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.

—In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS's transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

—Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance , link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem , a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance. Index Terms—Non-orthogonal multiple access (NOMA), adap-tive modulation and coding (AMC), hybrid automatic repeat request (HARQ), reinforcement learning (RL).

Unlike conventional cellular systems, the fifth generation (5gG) and beyond includes intrinsic support for vertical industries with diverse service requirements. Industrial process automation with autonomous fault detection and prediction, optimised operations and proactive control can be considered as one of the key verticals of 5G and beyond. Such applications enable equipping industrial plants with a reasoning sixth sense for optimised operations and fault avoidance. In this direction, we introduce an inter-disciplinary approach integrating wireless sensor networks with machine learning-enabled industrial plants to build a step towards developing this sixth sense technology, i.e., the reasoning ability. We develop a modular-based system that can be adapted to the vertical-specific elements. Without loss of generalisation, exemplary use cases are developed and presented including a fault detection/prediction scheme in a wireless communication network with sensors and actuators to enable the sixth sense technology with guaranteed service load requirements. The proposed schemes and modelling approach are implemented in a real chemical plant for testing purposes, and a high fault detection and prediction accuracy is achieved coupled with optimised sensor density analysis.

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10−4 times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering. Index Terms—Multicarrier-division duplex, cell-free massive MIMO, heterogeneous graph neural network, power allocation.

In this paper, we propose intelligent reflecting surface (IRS) aided multi-antenna physical layer security. We present a power efficient scheme to design the secure transmit power allocation and the surface reflecting phase shift. It aims to minimize the transmit power subject to the secrecy rate constraint at the legitimate user. Due to the non-convex nature of the formulated problem, we propose an alternative optimization algorithm and the semidefinite programming (SDP) relaxation to deal with this issue. Also, the closed-form expression of the optimal secure beamformer is derived. Finally, simulation results are presented to validate the proposed algorithm, which highlights the performance gains of the IRS to improve the secure transmission.

The LTE downlink multiuser multiple input multiple output (MIMO) systems are analyzed in this paper. Two spatial division multiplexing (SDM) multiuser MIMO schemes are investigated: Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the establishment of a mathematical model for the Signal to Interference plus Noise Ratio (SINR) distribution for multiuser SDM MIMO systems with frequency domain packet scheduler

In the literature, optimal power assuming Gaussian input has been evaluated in OFDM based Cognitive Radio (CR) systems to maximize the capacity of the secondary user while keeping the interference introduced to the primary user band within tolerable range. However, the Gaussian input assumption is not practical and Finite Symbol Alphabet (FSA) input distributions, i.e., M-QAM are used in practical systems. In this paper, we consider the power optimization problem under the condition of FSA inputs as used in practical systems, and derive an optimal power allocation strategy by capitalizing on the relationship between mutual information and minimum mean square error. The proposed scheme is shown to save transmit power in a CR system compared to its conventional counterpart, that assumes Gaussian input. In addition to extra allocated power, i.e., power wastage, the conventional power allocation scheme also causes nulling of more subcarriers, leading to reduced transmission rate, compared to the proposed scheme. The proposed optimal power algorithm is evaluated and compared with the conventional algorithm assuming Gaussian input through simulations. Numerical results reveal that for interference threshold values ranging between 1 mW to 3 mW, the transmit power saving with the proposed algorithm is in the range between 55-75%, 42-62% and 12-28% whereas the rate gain is in the range between 16.8-12.4%, 13-11.8% and 3-5.8% for BPSK, QPSK and 16-QAM inputs, respectively.

A beam steering (up to 36 degrees) high gain (20.5 dBi) Leaky-Wave Antenna (LWA) is presented at 26 GHz for enhanced data rate in millimeter wave (mm-wave) 5G system in dynamic environments. A low loss (

This paper investigates the performance of the uplink single carrier (SC) frequency division multiple access (FDMA) based linearly precoded multiuser multiple input multiple output (MIMO) systems with frequency domain packet scheduling. A mathematical expression of the received signal to interference plus noise ratio (SINR) for the studied systems is derived and a utility function based spatial frequency packet scheduling algorithms is investigated. The schedulers are shown to be able to exploit the available multiuser diversity in time, frequency and spatial domains.

Mixed-numerology transmission is proposed to support a variety of communication scenarios with diverse requirements. However, as the orthogonal frequency division multiplexing (OFDM) remains as the basic waveform, the peak-to average power ratio (PAPR) problem is still cumbersome. In this paper, based on the iterative clipping and filtering (ICF) and optimization methods, we investigate the PAPR reduction in the mixed numerology systems.We first illustrate that the direct extension of classical ICF brings about the accumulation of inter-numerology interference (INI) due to the repeated execution. By exploiting the clipping noise rather than the clipped signal, the noiseshaped ICF (NS-ICF) method is then proposed without increasing the INI. Next, we address the in-band distortion minimization problem subject to the PAPR constraint. By reformulation, the resulting model is separable in both the objective function and the constraints, and well suited for the alternating direction method of multipliers (ADMM) approach. The ADMM-based algorithms are then developed to split the original problem into several subproblems which can be easily solved with closedform solutions. Furthermore, the applications of the proposed PAPR reduction methods combined with filtering and windowing techniques are also shown to be effective.

Multiple input multiple output-Orthogonal frequency division multiplexing (MIMO-OFDM) is a viable solution for providing high data rate services in harsh channel environments. The optimum receivers for them are those based on the maximum likelihood criterion. However, they have a prohibitive complexity especially when channel dimensions are high and coding is employed. Zero Forcing (ZF) and Linear Minimum Mean Square Error (MMSE) receivers on the other hand provide practicable and low complexity solutions for detection, but require soft demappers to deduce the soft bits information contained in each of the received symbols. In this work, we present the ZF and MMSE receiver analysis of a bit interleaved and coded MIMO-OFDM system and propose a soft output demapper based on MMSE equalizer output to demap the information needed for viterbi decoding. A comparison of the proposed soft demapper with conventional soft demappers in literature show a significant performance improvement. We also noticed that it is more advantageous to apply the proposed demapper on a MIMO-OFDM system employing higher modulation schemes.

The future mobile networks will face challenges in support of heterogeneous services over a unified physical layer, calling for a waveform with good frequency localization. Filtered orthogonal frequency division multiplexing (f-OFDM), as a representative subband filtered waveform, can be employed to improve the spectrum localization of orthogonal frequency-division multiplexing (OFDM) signal. However, the applied filtering operations will impact the performance in various aspects, especially for narrow subband cases. Unlike existing studies which mainly focus its benefits, this paper investigates two negative consequences inflicted on single subband f-OFDM systems: in-band interference and filter frequency response (FFR) selectivity. The exact-form expression for the in-band interference is derived, and the effect of FFR selectivity is analyzed for both single antenna and multiple antenna cases. The in-band interference-free and nearly-free conditions for f-OFDM systems are studied. A low-complexity blockwise parallel interference cancellation (BwPIC) algorithm and a pre-equalizer are proposed to tackle the two issues caused by the filtering operations, respectively. Numerical results show that narrower subbands suffer more performance degradation compared to wider bands. In addition, the proposed BwPIC algorithm effectively suppresses interference, and pre-equalized f-OFDM (pf-OFDM) considerably outperforms f- OFDM in both single antenna and multi-antenna systems.

6G edge networks strive to offer ubiquitous intelligent services, requiring a greater emphasis on network stability and reliability. However, current networks present a low automation degree of the operation, administration and maintenance process. Consequently, active service migration away from abnormal network nodes and links, as well as automatic and transparent service recovery from sudden anomalies, become challenging tasks. These conditions underscore the urgency for an innovative service self-healing mechanism for 6G edge networks. Digital twin (DT) technology uses modeling to represent physical entities, thereby facilitating lifecycle management. However, the application of DT technology in networks is still a burgeoning field of study. In this paper, we explore the DT-driven service self-healing mechanism in 6G edge networks. Initially, we design a DT-based architecture for service self-healing. Subsequently, we construct a performance prediction mechanism leveraging graph neural networks (GNNs) to devise an efficient prediction model, which aims to accurately infer network performance and promptly detect abnormal network conditions. To maintain finegrained service stability amidst potential network anomalies, we propose a DT-driven service redeployment mechanism enhanced by GNNs. Comprehensive experimental results reveal that our proposed mechanism can accurately predict flow-level delays and identify abnormal links and nodes. Furthermore, the DT-driven service redeployment mechanism effectively reduces service delay and enhances network load balance.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients , and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

Device-to-device (D2D) and full duplex (FD) technologies demonstrate immense potentials to improve the spectral efficiency of wireless networks. However, their synergistic combination to reap added benefits is proved to be technically challenging. This letter investigates the throughput-maximization resource allocation problem for FD D2D communications underlaying cellular networks. The problem is decoupled into two subproblems, namely power allocation and channel assignment, which are solved by using a novel quadratic transform based scheme and the Kuhn-Munkres algorithm, respectively. Simulation results show that the proposed algorithm outperforms existing ones in terms of overall system throughput.

Flexibly supporting multiple services, each with different communication requirements and frame structure, has been identified as one of the most significant and promising characteristics of next generation and beyond wireless communication systems. However, integrating multiple frame structures with different subcarrier spacing in one radio carrier may result in significant inter-service-band-interference (ISBI). In this paper, a framework for multi-service (MS) systems is established based on subband filtered multi-carrier system. The subband filtering implementations and both asynchronous and generalized synchronous (GS) MS subband filtered multi-carrier (SFMC) systems have been proposed. Based on the GS-MS-SFMC system, the system model with ISBI is derived and a number of properties on ISBI are given. In addition, low-complexity ISBI cancelation algorithms are proposed by precoding the information symbols at the transmitter. For asynchronous MS-SFMC system in the presence of transceiver imperfections including carrier frequency offset, timing offset and phase noise, a complete analytical system model is established in terms of desired signal, intersymbol-interference, inter-carrier-interference, ISBI and noise. Thereafter, new channel equalization algorithms are proposed by considering the errors and imperfections. Numerical analysis shows that the analytical results match the simulation results, and the proposed ISBI cancelation and equalization algorithms can significantly improve the system performance in comparison with the existing algorithms.

Online variational bayesian filtering-based mobile target tracking in wireless sensor networks. Abstract: The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

In this paper, we present a novel Mutual Information (MI) based spatial frequency domain packet scheduling for downlink Orthogonal Frequency Division Multiple Access (OFDMA) multiuser MIMO systems. The proposed scheduler is designed to exploit the available multiuser diversity in time, frequency and spatial domains. The analysis model is based on the generalized 3GPP LTE downlink transmission for which two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated: Single User (SU) and Multi-user (MU) MIMO schemes. The results show that the proposed MU-MIMO scheduler is a more realistic solution and provides fairness among users for the system under consideration.

5G New Radio (NR) Release 15 has been specified in June 2018. It introduces numerous changes and potential improvements for physical layer data transmissions, although only point-to-point (PTP) communications are considered. In order to use physical data channels such as the Physical Downlink Shared Channel (PDSCH), it is essential to guarantee a successful transmission of control information via the Physical Downlink Control Channel (PDCCH). Taking into account these two aspects, in this paper, we first analyze the PDCCH processing chain in NR PTP as well as in the state-of-the-art Long Term Evolution (LTE) point-to-multipoint (PTM) solution, i.e., evolved Multimedia Broadcast Multicast Service (eMBMS). Then, via link level simulations, we compare the performance of the two technologies, observing the Bit/Block Error Rate (BER/BLER) for various scenarios. The objective is to identify the performance gap brought by physical layer changes in NR PDCCH as well as provide insightful guidelines on the control channel configuration towards NR PTM scenarios.

Orthogonal frequency division multiplexing (OFDM-IM) is a multicarrier transmission technology that modulates information bits not just onto subcarriers by means of Mary constellation mapping but also onto selected (active) subcarrier indices. Consequently, errors can occur in OFDMIM systems indices in addition to the errors of M-ary symbols. This paper analyzes the error scenarios and derives mathematical expressions for the error performance based on the maximum likelihood (ML) detection. In evaluating the bit error rate (BER) in the additive white Gaussian noise (AWGN) channel, some assumptions are made and our analytical result show that the BER of OFDM-IM system is a weighted sum of exponential functions and Q-functions. Our general BER expression has been shown to be in excellent agreement with numerical simulation and proven to be accurate and can serve as a reference for the design and evaluation of any arbitrary size and configuration of OFDM-IM systems.

This paper investigates the impact of intelligent reflecting surface (IRS) enabled wireless secure transmission. Specifically, an IRS is deployed to assist multiple-input multiple-output (MIMO) secure system to enhance the secrecy performance, and artificial noise (AN) is employed to introduce interference to degrade the reception of the eavesdropper. To improve the secrecy performance, we aim to maximize the achievable secrecy rate, subject to the transmit power constraint, by jointly designing the precoding of the secure transmission, the AN jamming, and the reflecting phase shift of the IRS. We first propose an alternative optimization algorithm (i.e., block coordinate descent (BCD) algorithm) to tackle the non-convexity of the formulated problem. This is made by deriving the transmit precoding and AN matrices via the Lagrange dual method and the phase shifts by the Majorization-Minimization (MM) algorithm. Our analysis reveals that the proposed BCD algorithm converges in a monotonically non-decreasing manner which leads to guaranteed optimal solution. Finally, we provide numerical results to validate the secrecy performance enhancement of the proposed scheme in comparison to the benchmark schemes.

In this letter, we design a wideband hybrid re-configurable intelligent surface network (WHRIS-Net) based on deep learning for reconfigurable intelligent surface (RIS)-assisted terahertz massive multiple-input multiple-output systems with beam squint. Firstly, the mean channel covariance matrices (MCCMs) from the base station to the RIS and from the RIS to the user are used as the inputs of WHRIS-Net. Then, a Phase-Net is applied to calculate the frequency-independent analog precoder and the phases of RIS elements. Finally, a Digital-Net designs the frequency-dependent digital precoder utilizing the MCCMs and the outputs of Phase-Net. Numerical simulations validate the effectiveness of the WHRIS-Net in terms of the sum rate and demonstrate its promising performance gain despite reduced average running time and feedback overhead. Index Terms—Terahertz (THz), reconfigurable intelligent surface (RIS), hybrid precoding, deep learning, beam squint.

A hybrid technique is proposed to manipulate the feld distribution in a substrate integrated waveguide (SIW) H-plane horn to enhance its radiation characteristics. The technique comprises two cascaded steps to govern the guided waves in the structure. The frst step is to correct the phase of felds and form a quasi-uniform distribution in the fare section so that the gain increases and sidelobe-level (SLL) decreases. This is obtained by loading the structure with a novel modulated metal-via lens. Field expansion on the radiating aperture of the SIW H-plane horn generates backward surface waves on both broad walls which increases the backlobe. In the second step, these backward surface waves are recycled and directed forward with the aid of holography theory. This is achieved by adding a couple of dielectric slabs with holographic-based patterns of metallic strips on both broad walls. With this step, the backlobe is reduced and the endfre gain is further increased. Using the proposed technique, the structure is designed and fabricated to operate at f = 30GHz which simultaneously improves the measured values of gain to 11.65 dBi, H-plane SLL to − 17.94 dB, and front-to-back ratio to 17.02 dB.

A simple-structure probe-fed multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) is designed for sub-6GHz applications with a reduced inter-element spacing (< 0.5λ). A 4-element rectangular DRA is positioned in a compact space verifying the proposed DRA potential for MIMO applications. Each element consists of two dielectric resonators with different permittivity of 5 and 10, excited by the coaxial probe. The measurement results reveal that the proposed MIMO DRA provides an envelope correlation coefficient (ECC) of less than 0.01 with good MIMO performance.

A new single-fed circularly polarized dielectric resonator antenna (CP-DRA) without beam squint is presented. The DRA comprises an S-shaped dielectric resonator (SDR) with a metalized edge and two rectangular dielectric resonators (RDRs) blocks. Horizontal extension section is applied as an extension of the SDR, and a vertical-section is placed in parallel to the metallic edge. A vertical coaxial probe is used to excite the SDR and the vertical RDR blocks through an S-shaped metal element and a small rectangular metal strip. The two added RDRs that form an L-shaped DR improve the radiation characteristics and compensate for the beam squint errors. A wideband CP performance is achieved due to the excitation of several orthogonal modes such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. The experimental results demonstrate an impedance bandwidth of approximately [Formula: see text] (3.71-7.45 GHz) and a 3-dB axial-ratio (AR) bandwidth of about [Formula: see text] (3.72-6.53 GHz) with a stable broadside beam achieving a measured peak gain of about [Formula: see text]. Furthermore, a 100% correction in beam squint value from [Formula: see text] to [Formula: see text] with respect to the antenna boresight is achieved.

With the vision of supporting global internet access, satellite communication has emerged as a key component for sixth-generation (6G) communication networks. A pivotal trend for satellite communication is using Low Earth Orbit (LEO) satellites to construct a space-based backbone network, which necessitates establishing multi-hop communication paths between satellites with certain inter-satellite routing strategies. However, the vast number of satellites and the fast-varying network topology, combined with the diversified traffic demands, pose significant challenges for inter-satellite routing. In this paper, we propose a novel inter-satellite routing scheme that employs Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to address the above-mentioned challenges. The overall objective is minimizing network-wide average end-to-end delay, which can be achieved by selecting paths with low aggregated propagation delay and balancing the load distribution among various links. Based on this fact, we formulate the inter-satellite routing problem, in which the satellite network is modeled as a graph. Then, we design a GNN and DRL integrated solution framework, where GNN is employed to learn the complex relationship between satellite nodes in the graph and DRL is employed to make routing decisions that are adaptive to the features of satellite networks. Simulation results demonstrate that our solution can adapt to the time-varying network dynamics and diverse traffic demands, outperforming benchmark schemes in terms of average end-to-end delay, reliability, and link utilization.

A multifunctional antenna with diverse radiation patterns in different frequency bands (2.45/5.8 GHz) is presented in this paper. The antenna has a low profile but exhibits an omni-directional radiation pattern in the low-band operation and uni-directional pattern in the high-band operation. For the high-band operation, a 2 x 2 patch arrays are designed by employing an out-of-phase feeding method. The low-band operation with the omni-directional pattern is achieved by exciting four open-ended slots in-phase. The four slots are slit in the ground of the high-band array and in this way, this footprint of the antenna is maintained. The operating principles of the antenna are studied with the aid of equivalent circuit model and the current distribution. The antenna is prototyped and measured, demonstrating good results in terms of bandwidths, inter-channel isolation, radiation characteristics.

In this paper, a comprehensive design and analysis of multiple-input multiple-output (MIMO) full-duplex (FD) relaying systems in a multi-cell environment are investigated, where a multi-antenna amplify-and-forward (AF) FD relay station serves multiple half-duplex (HD) multi-antenna users. The pivotal obstacles of loopback self-interference (LI) and multiple co-channel interferers (CCI) at the relay and destination when employing FD relaying in cellular networks are addressed. In contrast to the HD relaying mode, the CCI in the FD relaying mode is predicted to double since the uplink and downlink communications are simultaneously scheduled via the same channel. In this paper, the optimal layout of transmit (receive) precoding (decoding) weight vectors which maximizes the overall signal-to-interefernce-plusnoise ratio (SINR) is constructed by a suitable optimization problem, then a closed-form sub-optimal formula based on null space projection is presented. The proposed hop-by-hop rank- 1 zero-forcing (ZF) beamforming vectors are based on added ZF constraints used to suppress the LI and CCI channels at the relay and destination, i.e., the source and relay perform transmit ZF beamforming, while the relay and destination employ receive ZF combining. To this end, unified accurate expressions for the outage probability and ergodic capacity are derived in closed-form. In addition, simpler tight lower-bound formulas for the outage probability and ergodic capacity are presented. Moreover, the asymptotic approximations for outage probability is considered to gain insights into system behavior in terms of the diversity order and array gain. Numerical and simulation results show the accuracy of the presented exact analytical expressions and the tightness of the lower-bound expressions. The case of hopby- hop maximum-ratio transmission/maximal-ratio combining beamforming is included for comparison purposes. Furthermore, our results show that while multi-antenna terminals improve the system performance, the detrimental effect of CCI on FD relaying is clearly seen. Therefore, our findings unveil that MIMO FD relaying could significantly improve the system performance compared to its conventional MIMO HD relaying counterpart.

The paper proposes a novel design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) in a wireless powered Internet of Things (IoT) network, where two sensor node groups (SNGs) harvest energy from a power station (PS) and transmit their message to an access point (AP) with the harvested energy. The STAR-RIS, which is deployed in the middle of the SNGs and adopts the time splitting (TS) working mode, can help the energy transfer in the wireless energy transfer (WET) phase and the information transfer in the wireless information transfer (WIT) phase. The paper aims to maximize the sum throughput from the two SNGs to the AP by jointly designing the phase shifts of the STAR-RIS and the working time allocated to the two SNGs in the WET and WIT phases, respectively. To solve the formulated non-convex optimization problem, we propose a low-complexity algorithm where we first derive the optimal phase shifts of the STAR-RIS in the WIT phase. Then, we adopt the Lagrange dual method to simplify the optimization problem and optimize the phase shifts of the STAR-RIS in the WET phase by the Majorization-Minimization (MM) algorithm and the complex circle manifold (CCM) algorithm. Next, a two-layer iterative algorithm is used to obtain the optimal values of time allocated to the two SNGs. Finally, we evaluate the improvement of the proposed scheme by the simulation results compared with other benchmark schemes.

Supported by the expert-level advice of pioneering researchers, Orthogonal Frequency Division Multiple Access Fundamentals and Applications provides a comprehensive and accessible introduction to the foundations and applications of one of the most promising access technologies for current and future wireless networks. It includes authoritative coverage of the history, fundamental principles, key techniques, and critical design issues of OFDM systems. Covering various techniques of effective resource management for OFDM/OFDMA-based wireless communication systems, this cutting-edge reference: Addresses open problems and supplies possible solutions. Provides a concise overview of key techniques for adaptive modulation. Investigates radio channel modeling in OFDMA-based wireless communication systems. Details detection strategies of frequency-domain equalization for broadband communications. Introduces a novel combination of OFDM and the orbital angular momentum of the electromagnetic field to improve performance. Contains extensive treatment of adaptive MIMO beamforming suitable for multiuser access. This valuable resource supplies readers with a macro-level understanding of OFDMA and its key issues, while providing a systematic manual for those whose work is directly related to practical OFDMA and other multiuser communication systems projects.

In this paper, we investigate spectrum sharing ultra reliable and low-latency communications (URRLC) in an unmanned aerial vehicle (UAV)-aided cognitive radio (CR) internet of thing (IoT) network. Particularly, the secondary IoT devices opportunistically accesses the radio resource provided by a primary network and directly transmits short packets to the mobile UAV. A novel performance metric is proposed with finite block -length codes is adopted in the secondary UAV-aided IoT network. We aim to maximize the minimum average finite block -length rate for the secondary UAV-aided IoT network, subject to a probabilistic interference power constraint to the primary network based on imperfect channel state information (CSI). This formulated problem is non-convex due to the binary time scheduling, the power allocation, and the UAV altitude. In order to circumvent this issue, we develop an alternating method to solve this problem. Specifically, we first exploit the time scheduling optimization of the IoT devices for given power allocation and UAV altitude. Next, the monotonicity of the average finite block -length rate is analyzed to gain more insights for given time scheduling and UAV altitude. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the achievable average rate in the finite block length regime. The optimal altitude of the UAV can be obtained by one-dimensional line search. Numerical results validate the effectiveness and accuracy of the derived theoretical results.

The communication bottleneck severely constrains the scalability of distributed deep learning, and efficient communication scheduling accelerates distributed DNN training by overlapping computation and communication tasks. However, existing approaches based on tensor partitioning are not efficient and suffer from two challenges: (1) the fixed number of tensor blocks transferred in parallel can not necessarily minimize the communication overheads; (2) although the scheduling order that preferentially transmits tensor blocks close to the input layer can start forward propagation in the next iteration earlier, the shortest per-iteration time is not obtained. In this paper, we propose an efficient communication framework called US-Byte. It can schedule unequal-sized tensor blocks in a near-optimal order to minimize the training time. We build the mathematical model of US-Byte by two phases: (1) the overlap of gradient communication and backward propagation, and (2) the overlap of gradient communication and forward propagation. We theoretically derive the optimal solution for the second phase and efficiently solve the first phase with a low-complexity algorithm. We implement the US-Byte architecture on PyTorch framework. Extensive experiments on two different 8-node GPU clusters demonstrate that US-Byte can achieve up to 1.26x and 1.56x speedup compared to ByteScheduler and WFBP, respectively. We further exploit simulations of 128 GPUs to verify the potential scaling performance of US-Byte. Simulation results show that US-Byte can achieve up to 1.69x speedup compared to the state-of-the-art communication framework.

Massive machine-type communications (mMTC) are expected to be one of the most primary scenarios in the next-generation wireless communications and provide massive connectivity for Internet of Things (IoT). To meet the demanding technical requirements for mMTC, random access scheme with efficient joint activity and data detection (JADD) is vital. In this paper, we propose a compressive sensing (CS)-based grant-free random access scheme for mMTC, where JADD is formulated as a multiple measurement vectors (MMV) CS problem. By leveraging the prior knowledge of the discrete constellation symbols, we develop an orthogonal approximate message passing (OAMP)-MMV algorithm for JADD, where the structured sparsity is fully exploited for enhanced performance. Moreover, expectation maximization (EM) algorithm is employed to learn the unknown sparsity ratio of the a priori distribution and the noise variance. Simulation results show that the proposed scheme achieves superior performance over other state-of-the-art CS schemes.

In this paper, we present a novel sequence design for efficient channel estimation in multiple input multiple output filterbank multicarrier (MIMO-FBMC) system with offset QAM modulation. Our proposed sequences, transmitted over one FBMC/OQAM symbol, are real-valued in the frequency domain and display zero-correlation zone properties in the time-domain. The latter property enables optimal channel estimation for a least-square estimator in frequency-selective fading channels. To further improve the system performance, we mitigate the data interference by an iterative feedback loop between channel estimation and FBMC demodulation. Simulation results validate that our proposed real-valued orthogonal sequences and the iterative channel estimation and demodulation scheme provide a practical solution for enhanced performance in preamble-based MIMO-FBMC systems.

This paper presents the design and implementation of a high performance baseband transceiver targeted for System on a Chip (SoC). The presented architecture utilizes a 4×4 Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and is capable of enabling greater than 1Gbps wireless transmission. A complex channel equalization circuit is realized using matrix inversion via high-resolution QR decomposition. Full synthesis results are included as this MIMO-OFDM transceiver has been proved on standard FPGA technology.

This paper studies the optimum user selection scheme in a hybrid-duplex device-to-device (D2D) cellular networks. We derive an analytical integral-form expression of the cumulative distribution function (CDF) for the received signal-to-noise-plus-interference-ratio (SINK) at the D2D node, based on which the closed-form of the outage probability is obtained. Analysis shows that the proposed user selection scheme achieves the best SINK at the D2D node with interference to base station being limited by a pre-defined level. Hybrid duplex D2D can be switched between half and full duplex according to different residual self-interference to enhance the throughput of D2D pair. Simulation results are presented to validate the analysis.

Grant-free non-orthogonal multiple access (GF-NOMA) technique is considered as a promising solution to address the bottleneck of ubiquitous connectivity in massive machine type communication (mMTC) scenarios. One of the challenging problems in uplink GF-NOMA systems is how to efficiently perform user activity detection and data detection. In this paper, a novel complexity-reduction weighted block coordinate descend (CR-WBCD) algorithm is proposed to address this problem. To be specific, we formulate the multi-user detection (MUD) problem in uplink GF-NOMA systems as a weighted l_{2} minimization problem. Based on the block coordinate descend (BCD) framework, a closed-form solution involving dynamic user-specific weights is derived to adaptively identify the active users with high accuracy. Furthermore, a complexity reduction mechanism is developed for substantial computational cost saving. Simulation results demonstrate that the proposed algorithm enjoys bound-approaching detection performance with more than three-order of magnitude computational complexity reduction.

This letter presents a systematic method to regulating the response of an artificial impedance surface. The method is based on governing the dispersion diagram to control the depth of modulation so that a meaningful pattern of scatterers is obtained on the structure. The method is applied on a holographic-based large reflective metasurface to achieve a dual-beam radiation pattern with tilt angles of \pm 45^{\circ } in the azimuth plane at f=3.5 GHz. The structure is fabricated and the measured data concur with the simulation results.

The maximum signal-to-interference plus noise ratio (MSINR) design criterion is proposed in this paper to maximize the geometric product of signal-to-interference plus noise ratio (GEOM-SINR) in non-regenerative MIMO relay system. It is shown that the optimal MSINR based precoding at relay will diagonalize the equivalent source-relay-destination channel into parallel sub-channels, and the MSINR based MIMO relay precoding design will be transformed into the MSINR criterion based power allocation among multiple sub-channels. Simulation results are presented to corroborate the MSINR-based MIMO relay precoding design. It is unveiled that, compared with the existing maximal mutual information (MMI) based MIMO relay precoding design and the minimal mean square error (MMSE) based MIMO relay precoding design, MSINR-based MIMO relay precoding design is able to achieve a better tradeoff between the communication reliability and the realized ergodic capacity. © 2012 IEEE.

This work provides the first link level performance evaluation of the Rate-Splitting (RS) based precoding scheme in a downlink multi-user multiple input single output (MU-MISO) system. Contrary to the existing works on the RS precoding that mostly focused on the sum rate or minimum rate maximization, this work bridges the optimization results with the bit error rate (BER) performance, initiating the RS software implementation. We demonstrate that, in an overloaded scenario, the conventional precoding schemes suffer from the BER error floor that corresponds to their rate saturation, which can be overcome by the RS-based strategy that adding the message decodability of certain users with the interference-limited message rate.

In this paper, we introduce the concept of average per-user rate to the multiuser Multiple-Input, Multiple-Output (MIMO) system with the frequency domain packet scheduler (FDPS) at base stations, which provides an estimate of the rate that the system could provide for each admitted user. The proposed admission control is designed by comparing the user's quality of service (QoS) requirements with the transmission rate that the system can offer. The analytical model is based on the generalized 3GPP LTE downlink transmission for which two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated, namely, Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the derivation of the achievable rate for each user in the SDM MIMO systems based on a mathematical model of the Signal to Interference plus Noise Ratio (SINR) distribution with the frequency domain packet scheduler. The achievable rate provides insights into the system's performance from a different perspective. © 2012 IEEE.

—Non-terrestrial networks (NTNs) are expected to play a pivotal role in the future wireless ecosystem. Due to its high-dynamic characteristics, the accurate estimation and compensation of carrier frequency offset (CFO) are crucial for supporting 5G new radio (NR) enabled satellite direct access. With emphasis on ensuring reliable uplink synchronization, we propose a clustering-neural network based CFO estimation scheme by virtue of NR random access preambles. By leveraging the sparsity and regularity of input samples, the proposed scheme can achieve fast and precise prediction of CFOs, while establishing robustness against time uncertainty and channel variation within a satellite beam. Simulation results validate the feasibility of our scheme in various NTN scenarios, and demonstrate its superiority in terms of stable estimation performance over the existing schemes. Index Terms—Non-terrestrial networks, carrier frequency offset estimation, random access preamble, clustering, neural network .

Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (OFDM-IM), which conveyed information bits via the activated indices and constellation symbols is a promising technique in the next wireless communications. In the OFDM-IM scheme, only part of subcarriers are activated to transmit information, the inactive subcarriers transmit zero symbols, so that the conventional differential coding is not suitable for the adjacent subcarriers. In order to address this issue, in this paper, a novel Rectangular Differential OFDM-IM (RD-OFDM-IM) scheme is proposed to exploit the benefits of OFDM-IM dispensing with Channel State Information (CSI). In the proposed RD-OFDM-IM scheme, N subcarriers are partitioned into G subblocks and index modulation is employed in each subblock first. Then rectangular differential coding is invoked during two adjacent subblocks, so that nocoherent detection can be employed for the proposed RD-OFDM-IM scheme. Simulation results are shown that the proposed RD-OFDM-IM scheme is capable of providing considerable performance gain over conventional Differential OFDM (D-OFDM) scheme with lower Peak Average Power Ratio (PAPR).

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overloading performance of the BS, we develop a general framework for the synergistic amalgamation of the spatial division multiple access (SDMA) technique with the CS-based grant-free NOMA. We derive a closed-form statistical beamforming and a dynamic beamforming scheme for the inter-cluster interference suppression when applying SDMA. Based on this, we further develop a joint adaptive beamforming and subspace pursuit (JABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level. To further improve the data recovery performance, we propose an interference cancellationbased J-ABF-SP scheme (J-ABF-SP-IC) by using the initial signal estimates generated from the J-ABF-SP algorithm. Illustrative simulations verify the superior user detection and signal recovery performance of our proposed algorithms in comparison with existing CS-based grant-free NOMA techniques.

The flexibility in supporting heterogeneous services with vastly different technical requirements is one of the distinguishing characteristics of the fifth generation (5G) communication systems and beyond. One viable solution is to divide the system bandwidth into several bandwidth parts (BWPs), each having a distinct numerology optimized for a particular service. However, multiplexing of mixed numerologies over a unified physical infrastructure comes at the cost of induced interference. In this paper, we develop an analytical system model for inter-numerology interference (InterNI) analysis in orthogonal frequency-division multiplexing (OFDM) systems with and without filter processing in the presence of mixed numerologies. With the analytical model, the level of InterNI is quantified by the developed analytical metric, which is expressed as a function of several system parameters. This leads to an analysis and evaluation of these parameters for meeting a given distortion target. Moreover, a case study on power allocation utilizing the derived analysis is presented, where an optimization problem of maximizing the sum rate is formulated, and a solution is also provided. It is also demonstrated that a filtered-OFDM system better accommodates the coexistence of mixed numerologies. The proposed model provides an accurate analytical guidance for the multi-service design in 5G and beyond systems.

This paper proposes a complex-valued discrete multicarrier modulation (MCM) system based on the real-valued discrete Hartley transform (DHT) and its inverse (IDHT). Unlike conventional discrete Fourier transform (DFT), DHT can not diagonalize the multipath fading channel due to its inherent properties, which results in the mutual interference between subcarriers in the same mirror-symmetrical pair.We explore the interference pattern in order to seek an optimal solution to utilize the channel diversity for the purpose of enhancing system bit error performance (BEP). It is shown that the optimal channel diversity gain can be achieved via a pairwise maximum likelihood (ML) detection, taking into account not only the subcarrier’s own channel quality but also the channel state of its mirror-symmetrical peer. Performance analysis indicates that DHT-based MCM mitigates the fast fading effect by averaging the channel power gain on the mirror-symmetrical subcarriers. Simulation results show that the proposed system has a substantial improvement in BEP over conventional DFT-Based MCM.

This paper presents a novel design of trapped microstrip-ridge gap waveguide by using partially filled air gaps in a conventional microstrip-ridge gap waveguide. The proposed method offers an applicable solution to obviate frustrating assembly processes for standalone high-frequency circuits employing the low temperature co-fired ceramics technology which supports buried cavities. To show the practicality of the proposed approach, propagation characteristics of both trapped microstrip and microstrip-ridge gap waveguide are compared first. Then, a right-angle bend is introduced, followed by designing a power divider. These components are used to feed a linear 4-element array antenna. The bandwidth of the proposed array is 13 GHz from 64~76 GHz and provides the realized gain of over 10 dBi and the total efficiency of about 80% throughout the operational band. The antenna is an appropriate candidate for upper bands of WiGig (63.72~70.2) and FCC-approved 70 GHz band (71~76 GHz) applications.

In this paper, a high flat gain waveguide-fed aperture antenna has been proposed. For this purpose, two layers of FR4 dielectric as superstrates have been located in front of the aperture to enhance the bandwidth and the gain of the antenna. Moreover, a conductive shield, which is connected to the edges of the ground plane and surrounding aperture and superstrates, applied to the proposed structure to improve its radiation characteristics. The proposed antenna has been simulated with HFSS and optimized with parametric study and the following results have been obtained. The maximum gain of 13.0 dBi and 0.5-dBi gain bandwidth of 25.9 % (8.96 - 11.63 GHz) has been achieved. The 3-dBi gain bandwidth of the proposed antenna is 40.7% (8.07-12.20 GHz), which has a suitable reflection coefficient (

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

This paper is concerned with the key features and fundamental technology components for 5G New Radio (NR) for genuine realization of connected and cooperative autonomous driving. We discuss the major functionalities of physical layer, Sidelink features and its resource allocation, architecture flexibility, security and privacy mechanisms, and precise positioning techniques with an evolution path from existing cellular vehicle-to-everything (V2X) technology towards NR-V2X. Moreover, we envisage and highlight the potential of machine learning for further enhancement of various NR-V2X services. Lastly, we show how 5G NR can be configured to support advanced V2X use cases in autonomous driving.

In this communication, a low-cost, single-pole double-throw (SPDT) filtering radio frequency (RF) switch based on coupled resonators is proposed and utilized in a novel pattern reconfigurable antenna design. The proposed filtering switch employs two second-order quarter-wavelength microstrip resonant structures which can be controlled by p-i-n diodes, respectively. Compared with traditional switches based on p-i-n diode, this filtering switch can not only improve the RF response but also suppress the high-order harmonic, when it is used with an antenna. Then, a low-profile dual-port broadside/conical dual-mode antenna is proposed by embedding a slotted substrate integrated waveguide (SIW) cavity in the middle of a patch antenna. The slotted cavity is to realize the radiation in the broadside whereas the patch is fed by two probes in-phase to realize the conical radiation. Finally, the filtering switch and the dual-mode antenna are combined to realize the pattern reconfigurable antenna. By controlling the states of the p-i-n diodes, broadside and conical radiation patterns can be easily switched. The concept of switch-based pattern reconfigurable antenna is prototyped and experimentally verified. Measured results agree with the simulations, demonstrating a promising solution for pattern reconfigurable antenna designs.

This article proposes a novel machine-learning-based routing optimization for the multiple reconfigurable intelligent surfaces (M-RIS)-assisted multihop cooperative networks, in which a practical phase model for reconfigurable intelligent surface (RIS) with the amplitude variation based on the corresponding discrete phase shift is considered. We aim to maximize the end-to-end data rate in the proposed network by jointly optimizing the data transmission path, the passive beamforming design of RIS, and transmit power allocation. To tackle this complicated nonconvex problem, we divide it into two subtasks: 1) the passive beamforming design of the RIS and 2) joint routing and power allocation optimization. First, for the passive beamforming design of RIS, we develop a distributed learning algorithm that employs a cascade forward backpropagation network in each relay node to solve the RIS coefficients optimization problem by directly using the optimization target to train the cascade networks. This solution can avoid the curse of dimensionality of traditional reinforcement learning algorithms in the RIS optimization problem. Then, based on the result of RIS optimization, we introduce the proximal policy optimization (PPO) algorithm with the clipping method to find solutions for joint optimization of routing and power allocation via achieving the long-term benefit in the Markov decision process (MDP). Simulation results show that the proposed learning-based scheme can learn from the environment to improve its policy stability and efficiency in the iterative training process for optimizing routing and RIS and significantly outperform the benchmark schemes.

This paper investigates joint complex diversity coding (CDC) and error control coding (ECC) to increase diversity gains across multiple antennas, OFDM blocks, and OFDM sub-carriers. A general diversity analysis for joint CDC and ECC based space-time-frequency codes (STFCs) is provided. The mapping designs from ECC to CDC are crucial for efficient exploitation of the diversity potential. This paper provides and proves a sufficient condition of full diversity construction with joint 3D CDC and ECC, bit-interleaved coded complex diversity coding (BICCDC) and symbol-interleaved coded complex diversity coding (SICCDC). A multi-stream architecture is also introduced to reduce the complexity and latency of the decoding process. © 2011 IEEE.

Decentralized joint transmit power and beam- forming selection for multiple antenna wireless ad hoc net- works operating in a multi-user interference environment is considered. An important feature of the considered environ- ment is that altering the transmit beamforming pattern at some node generally creates more signicant changes to in- terference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence be- havior of the proposed algorithm compared to the best re- sponse and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.

In order to achieve millimeter wave (mmWave) beam alignment , a class of beam scanning and searching schemes have been extensively studied [1–3]. Recently, to address the problems of the traditional algorithms have a high sample complexity, some adaptive beam scanning approaches utilize the hierarchical beamforming codebook to reduce the training time at the cost of frequent feedback [2]. Then, to eliminate the feedback link, a random beam alignment algorithm is proposed by utilizing the pseudo-random spreading codes [3]. However, it needs a Pseudo-Noise (PN) sequences with sufficient length to ensure the good correlation properties of different beams. Furthermore, in addition to the above disadvantages, most of the existing algorithms require either a separate pilot sequence per user or long beam scanning time when considering mmWave multiuser uplinking systems. To solve the above problems, a novel class of beam alignment algorithms based on the sparse graph coding theory are proposed in this paper. Firstly, we investigate the uplink mmWave beam training structure. Based on the analysis, the mmWave multiuser beam alignment problem is transformed into the sparse-graph design and detection problem. Secondly, a beam alignment algorithm framework based on sparse-graph coding and decoding is proposed. Furthermore , we derive the theoretical bound to chose the optimal parameters of the designed coding matrix. Finally, two beam alignment algorithms are proposed to detect the beam index in different settings. Simulation results confirm that our beam algorithms outperform the conventional beam training methods. Proposed Uplink Beam Training Scheme. This paper considers a typical uplink mmWave MU-MIMO system, where the BS communicates with K UEs simultaneously. Suppose that the BS is equipped with N R antennas and N RF RF chains, while the k-th UE has M T antennas and M RF RF chains. Then, the channel associated with the k-th UE can be given by [4]

In this paper, we propose to use golden angle modulation (GAM) points to construct codebooks for uplink and downlink sparse code multiple access (SCMA) systems. We provide two categories of codebooks with one and two optimization parameters respectively. The advantages of the proposed design method are twofold: 1) the number of optimization variables is independent of codebook and system parameters; 2) it is simple to implement. In the downlink, we use GAM points to build a multidimensional mother constellation for SCMA codebooks, while in the uplink GAM points are directly mapped to user codebooks. The proposed codebooks exhibit good performance with low peak to average power ratio (PAPR) compared to the codebooks proposed in the literature based on constellation rotation and interleaving.

Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress. Since there is a lack of literature on the security and reliability issues of LEO Satellite Communication Systems (SCSs), we aim to fill this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and elaborate on their security and reliability requirements. In light of this, we further discuss their vulnerabilities, including potential security attacks launched against them and reliability risks, followed by outlining the associated lessons learned. Subsequently, we discuss the corresponding security and reliability enhancement solutions, unveil a range of trade-offs, and summarize the lessons gleaned. Furthermore, we shed light on several promising future research directions for enhancing the security and reliability of LEO SCSs, such as integrated sensing and communication, computer vision aided communications, as well as challenges brought about by megaconstellation and commercialization. Finally, we summarize the lessons inferred and crystallize the take-away messages in our design guidelines.

A convolutionally coded M-ary orthogonal direct sequence code division multiple access (DS-CDMA) system in time-varying frequency-selective Rayleigh fading channels is considered in this work. We propose several novel soft demodulation algorithms based on interference cancellation and suppression techniques that can be coupled with soft decoding to improve the system performance in an iterative manner. The performance of the proposed demodulation algorithms is evaluated numerically and proved to achieve substantial bit error rate (BER) performance gain compared with the conventional detection schemes.

A novel interference cancellation (IC) scheme for MIMO MC-CDM systems is proposed. It is shown that the existing IC schemes are suboptimum and their performance can be improved by utilising some special properties of the residual interference after interference cancellation.

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes.

The rapid development of modern communication services results in high data rate requirements from the end user. It is challenging to meet high data rate requirements because of prevailing issues such as spectrum scarcity and spectrum underutilization due to fixed spectrum assignment policy. Cognitive Radio (CR), being the enabler of dynamic spectrum management techniques, has the capability to tackle these issues by proficiently implementing spectrum sharing schemes using Multicarrier Modulation (MCM) techniques. In CR system, where the Primary User (PU) and the Secondary User (SU) co-exist in the same frequency band, mutual interference (i.e., from SU to PU and vice versa) is a limiting factor on the achievable capacity of both the PU and the SU. Power allocation in MCM based CR systems aims to dynamically control the transmit power on each subcarrier of the SU in order to reduce the mutual interference. Furthermore, combining multiple antennas with MCM is regarded as a very attractive solution for the CR communications to effectively enhance data rate without demanding additional bandwidth and transmit power.

This paper presents a new technique for designing Multiple Input Multiple (MIMO) Output antennas having pattern diversity. Massive MIMO is expected to form part of 5G communications and will require antennas having a very large number of elements. However, due to the size limitation, it is highly challenging to preserve high isolation between the ports. Pattern diversity technique are also highly desirable and can facilitate MIMO systems with diversity gain. However, achieving that within a compact antenna where there is limited space between the elements is also challenging. In this paper a technique is introduce and applied to 4-element and 6-element MIMO antennas. This technique can improve the isolation between the ports and it also yields pattern diversity for MIMO antennas with various numbers of elements. The technique is verified via experimental measurement.

The bit error rate performance of broadband wireless fixed access (FWA) systems over multipath fading channels is investigated in this paper. Linear MMSE equalization is examined theoretically for 16-QAM and QPSK modulated FWA systems and shown to yield unsatisfactory performance. The theoretical analysis is validated by Monte-Carlo simulations and proved to be reasonably accurate. It provides us an insight into the physical limitations imposed by the FWA channels and suggest solutions to improve the capacity and performance of future FWA systems. Copyright © 2006 John Wiley & Sons, Ltd.

—Physical layer security (PLS) is a promising technique to improve the security of wireless communications. However , when the legitimate user's and eavesdropper's channels are strongly correlated, directly applying PLS might no longer be a valid approach. In this paper, by introducing the reconfigurable intelligent surface (RIS), we study how to design access point (AP) and RIS beamforming and deploy RIS to improve the security under strong channel correlation. Furthermore, by taking into consideration the " multiplicative fading " effect, we formulate the secrecy rate (SR) maximization problem in the passive and active RIS cases. Next, we propose a semidefinite program (SDR)-based alternative optimization (AO) algorithm for each case, respectively. However, the computational complexity of the SDR approach is prohibitive for the large-size RIS. To tackle this issue, we respectively develop the low-complexity minorization maximization-based and primal-dual subgradient-based AO algorithms for two cases. Finally, we analyze the effect of the RIS deployment on the SR, and simulations results demonstrate the effectiveness of the proposed schemes.

The contributions of this paper are twofold: Firstly, we introduce a novel class of sequence pairs, called “cross Zcomplementary pairs (CZCPs)”, each displaying zero-correlation zone (ZCZ) properties for both their aperiodic autocorrelation sums and crosscorrelation sums. Systematic constructions of perfect CZCPs based on selected Golay complementary pairs (GCPs) are presented. Secondly, we point out that CZCPs can be utilized as a key component in designing training sequences for broadband spatial modulation (SM) systems. We show that our proposed SM training sequences derived from CZCPs lead to optimal channel estimation performance over frequency-selective channels.

The discrete cosine transform (DCT) based multicarrier system is regarded as one of the complementary multicarrier transmission techniques for 5th Generation (5G) applications in near future. By employing cosine basis as orthogonal functions for multiplexing each real-valued symbol with symbol period of T , it is able to reduce the minimum orthogonal frequency spacing to 1/(2T ) Hz, which is only half of that compared to discrete Fourier transform (DFT) based multicarrier systems. Critical to the optimal DCT-based system design that achieves interference-free single-tap equalization, not only both prefix and suffix are needed as symmetric extension of information block, but also a so-called front-end pre-filter is necessarily introduced at the receiver side. Since the pre-filtering process is essentially a time reversed convolution for continuous inputs, the output signal-to-noise ratio (SNR) for each subcarrier after filtering is enhanced. In this paper, the impact of pre-filtering on the system performance is analyzed in terms of ergodic output SNR per subcarrier. This is followed by reformulated detection criterion where such filtering process is taken into consideration. Numerical results show that under modified detection criteria, the proposed detection algorithms improve the overall bit error rate (BER) performance effectively.

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multipleoutput (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user’s bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users’ end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under per-access point (AP) power constraints. The max-min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel’s coherence time and on the total number of users.

Satellites will play an indispensable part in 5G roll out and the common use of new radio (NR) air interface will enable this. Satellite-terrestrial integration requires adaptations to the existing NR standards and demands further study on the potential areas of impact. From a physical layer perspective, the candidate waveform has a critical role in addressing design constraints to support non-terrestrial networks (NTN). In this paper, the adaptability of frequency-localized orthogonal frequency division multiplexing (OFDM)-based candidate waveforms and solutions are discussed in the context of physical layer attributes of non-linear satellite channel conditions. The performance of the new air interface waveforms are analysed in terms of spectral confinement, peak-to-average power ratio (PAPR), power amplifier efficiency, robustness against non-linear distortions and carrier frequency offset (CFO).

In this paper, we presents a novel method of turbo equalization and decoding multi-level trellis coded modulation (TCM) signals over frequency selective channels. Results show that the proposed algorithm achieves better performance with reduced complexity compared to previous work on the MMSE filter-based turbo equalization for non-binary coded modulation scheme. The performance gain is accomplished by passing the refined signal from different paths to the TCM decoder as channel value in addition to the a prior information. While the computational complexity is reduced by avoiding matrix inversion for each symbol estimate.

This paper introduces a dual-band rectangular dielectric resonator antenna (RDRA) design for beyond-fifth-generation (B5G) applications. The RDRA operates at both sub-6 GHz and millimeter-wave (mm-wave) bands, featuring a high permittivity of 19.8 for miniaturized volume. Additionally, we introduce an innovative dual-band RDRA design, which incorporates a rectangular dielectric resonator (RDR) and a ceramic/polymer structure to enable two distinct operating frequencies , i.e., 3.5 GHz and 28 GHz, respectively. Simulations are performed using ANSYS HFSS software, achieving bandwidth of 7.7% and 17.8% and gain of 4.6 dBi and 7.6 dBi for the sub-6 GHz and mm-wave structures, respectively. The performance of the sub-6 GHz RDRA structure is demonstrated through a combination of simulation and prototype experimental validation. A bandwidth of 9.19% with an impedance match of-43 dB is achieved at the resonant frequency of 3.59 GHz. A peak gain of 4.72 dBi is also demonstrated.

The development of modellings and analytical tools to structurise and study the allocation of resources through noble user competitions become essential, especially considering the increased degree of heterogeneity in application and service demands that will be cornerstone in future communication systems. Stochastic asymmetric Blotto games appear promising to modelling such problems, and devising their Nash equilibrium (NE) strategies by anticipating the potential outcomes of user competitions. In this regard, this paper approaches the generic energy efficiency problem with a new stochastic asymmetric Blotto game paradigm to enable the derivation of joint optimal bandwidth and transmit power allocations by setting multiple users to compete in multiple auction-like contests for their individual resource demands. The proposed modelling innovates by abstracting the notion of fairness from centrally-imposed to distributed-competitive, where each user’s pay-off probability is expressed as quantitative bidding metric, so as, all users’ actions can be interdependent, i.e., each user attains its utility given the allocations of other users, which eliminates the chance of low valued carriers not being claimed by any user, and, in principle, enables the full utilisation of wireless resources. We also contribute by resolving the allocation problem with low complexity using new mathematical techniques based on Charnes-Cooper transformation, which eliminate the additional coefficients and multipliers that typically appear during optimisation analysis, and derive the joint optimal strategy as a set of linear single-variable functions for each user. We prove that our strategy converges towards a unique, monotonous and scalable NE, and examine its optimality, positivity and feasibility properties in detail. Simulation comparisons with relevant studies confirm the superiority of our approach in terms of higher energy efficiency performance, fairness index and quality-of-service provision.

It has been claimed that the filter bank multicarrier (FBMC) systems suffer from negligible performance loss caused by moderate dispersive channels in the absence of guard time protection between symbols. However, a theoretical and systematic explanation/analysis for the statement is missing in the literature to date. In this paper, based on one-tap minimum mean square error (MMSE) and zero-forcing (ZF) channel equalizations, the impact of doubly dispersive channel on the performance of FBMC systems is analyzed in terms of mean square error (MSE) of received symbols. Based on this analytical framework, we prove that the circular convolution property between symbols and the corresponding channel coefficients in the frequency domain holds loosely with a set of inaccuracies. To facilitate analysis, we first model the FBMC system in a vector/matrix form and derive the estimated symbols as a sum of desired signal, noise, inter-symbol interference (ISI), inter-carrier interference (ICI), inter-block interference (IBI) and estimation bias in the MMSE equalizer. Those terms are derived one-by-one and expressed as a function of channel parameters. The numerical results reveal that in harsh channel conditions, e.g., with large Doppler spread or channel delay spread, the FBMC system performance may be severely deteriorated and error floor will occur.

In Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radio systems, power optimization algorithms have been evaluated to maximize the achievable data rates of the Secondary User (SU). However, unrealistic assumptions are made in the existing work, i.e. a Gaussian input distribution and traditional interference model that assumes frequency division multiplexing modulated Primary User (PU) with perfect synchronization between the PU and the SU. In this paper, we first derive a practical interference model by assuming OFDM modulated PU with imperfect synchronization. Based on the new interference model, the power optimization problem is proposed for the Finite Symbol Alphabet (FSA) input distribution (i.e., M-QAM), as used in practical systems. The proposed scheme is shown to save transmit power and to achieve higher data rates compared to the Gaussian optimized power allocation and the uniform power loading schemes. Furthermore, a theoretical framework is established in this paper to estimate the power saving by evaluating optimal power allocation for the Gaussian and the FSA input. Our theoretical analysis is verified by simulations and proved to be accurate. It provides guidance for the system design and gives deeper insights into the choice of parameters affecting power saving and rate improvement.

A high gain (20 dBi) Leaky-Wave Antenna (LWA) is presented at 26 GHz with beam steering capabilities (44°) for high data throughput in millimeter-wave (mm-wave) 5G systems. A tunable phase shifting High Impedance Surface (HIS) exhibiting low loss (

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.

Abstract—Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Dierent from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is dicult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.

Grant-free non-orthogonal multiple access (NOMA) scheme is a promising candidate to accommodate massive connectivity with reduced signalling overhead for Internet of Things (IoT) services in massive machine-type communication (mMTC) networks. In this paper, we propose a low-complexity compressed sensing (CS) based sparsity adaptive block gradient pursuit (SA-BGP) algorithm in uplink grant-free NOMA systems. Our proposed SA-BGP algorithm is capable of jointly carrying out channel estimation (CE), user activity detection (UAD) and data detection (DD) without knowing the user sparsity level. By exploiting the inherent sparsity of transmission signal and gradient descend, our proposed method can enjoy a decent detection performance with substantial reduction of computational complexity. Simulation results demonstrate that the proposed method achieves a balanced trade-off between computational complexity and detection performance, rendering it a viable solution for future IoT applications.

—This paper investigates the covert performance for an unmanned aerial vehicle (UAV) jammer assisted cognitive radio network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN) assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then a GAN based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of UAV's trajectory and transmit power to maximize the covert rate and the probability of detection errors. Our simulation results show that, the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of UAV's trajectory and transmit power for the covert communication.

The fifth-generation (5G) new radio (NR) cellular system promises a significant increase in capacity with reduced latency. However, the 5G NR system will be deployed along with legacy cellular systems such as the long-term evolution (LTE). Scarcity of spectrum resources in low frequency bands motivates adjacent-/co-carrier deployments. This approach comes with a wide range of practical benefits and it improves spectrum utilization by re-using the LTE bands. However, such deployments restrict the 5G NR flexibility in terms of frame allocations to avoid the most critical mutual adjacent-channel interference. This in turns prevents achieving the promised 5G NR latency figures. In this we paper, we tackle this issue by proposing to use the minislot uplink feature of 5G NR to perform uplink acknowledgement and feedback to reduce the frame latency with selective blind retransmission to overcome the effect of interference. Extensive system-level simulations under realistic scenarios show that the proposed solution can reduce the peak frame latency for feedback and acknowledgment up to 33% and for retransmission by up to 25% at a marginal cost of an up to 3% reduction in throughput.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

In this paper, a golden angle modulation (GAM) aided differential orthogonal frequency division multiplexing with index modulation (DOFDM-IM-GAM) system for low-earth orbit (LEO) satellite communications is proposed, which amalgamates the concept of differential coding to exploit the advantage of GAM and OFDM-IM without channel state information (CSI) at the receiver. Specifically, in the DOFDM-IM-GAM system, L subcarriers are divided into Q subblocks, and differential coding based on power normalization is applied to adjacent subblocks. Furthermore, the average bit error probability (ABEP) and mutual information (MI) of the proposed DOFDM-IM-GAM system are derived over the Rayleigh fading channel and LEO satellite channel. Simulation results demonstrate that the proposed DOFDM-IM-GAM system offers significant performance improvements compared to conventional DOFDM-IM systems.

Towards 6G networks, such as virtual reality (VR) applications, Industry 4.0 and automated driving, demand mobile edge computing (MEC) techniques to offload computing tasks to nearby servers, which however causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, so that enabling low latency and high throughput. For this sake, this paper investigates the resource management for the offload transmission of mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation and power allocation is formulated as a multi-objective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using -constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that, the gap between Pareto front generated by three-stage iterative resource allocation algorithm and the real Pareto front less than 0.16%. Futher, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-2, while significantly outperforms the other traditional schemes.

With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally. In this context, the emerging low-Earth-orbit satellites (LEO-SATs) have been regarded as a promising enabler to complement terrestrial wireless networks in providing ubiquitous connectivity and bridging the ever-growing digital divide in the expected next-generation wireless communications. Nevertheless, the harsh conditions posed by LEO-SATs have imposed significant challenges to the current multiple access (MA) schemes and led to an emerging paradigm shift in system design. In this article, we first provide a comprehensive overview of the state-of-the-art MA schemes and investigate their limitations in the context of LEO-SATs. To this end, we propose a novel next generation MA (NGMA), which amalgamates the grant-free non-orthogonal multiple access (GF-NOMA) mechanism and the orthogonal time frequency space (OTFS) waveform, for simplifying the connection procedure with reduced access latency and enhanced Doppler-robustness. Critical open challenging issues and future directions are finally presented for further technical development.

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

In Cognitive Radio (CR) systems, the data rate of the Secondary User (SU) can be maximized by optimizing the transmit power, given a threshold for the interference caused to the Primary User (PU). In conventional power optimization algorithms, the Gaussian input distribution is assumed, which is unrealistic, whereas the Finite Symbol Alphabet (FSA) input distribution, (i.e., M-QAM) is more applicable to practical systems. In this paper, we consider the power optimization problem in multiple input multiple output orthogonal frequency division multiplexing based CR systems given FSA inputs, and derive an optimal power allocation scheme by capitalizing on the relationship between mutual information and minimum mean square error. The proposed scheme is shown to save transmit power compared to its conventional counterpart. Furthermore, our proposed scheme achieves higher data rate compared to the Gaussian optimized power due to fewer number of subcarriers being nulled. The proposed optimal power algorithm is evaluated and compared with the conventional power allocation algorithms using Monte Carlo simulations. Numerical results reveal that, for distances between the SU transmitter and the PU receiver ranging between 50m to 85m, the transmit power saving with the proposed algorithm is in the range 13-90%, whereas the rate gain is in the range 5-31% depending on the modulation scheme (i.e., BPSK, QPSK and 16-QAM) used.

Recently proposed universal filtered multi-carrier (UFMC) system is not an orthogonal system in multipath channel environments and might cause significant performance loss. In this paper, we propose a cyclic prefix (CP) based UFMC system and first analyze the conditions for interference-free one-tap equalization in the absence of transceiver imperfections. Then the corresponding signal model and output SNR (signal-tonoise ratio) expression are derived. In the presence of carrier frequency offset (CFO), timing offset (TO) and insufficient CP length, we establish an analytical system model as a summation of desired signal, inter-symbol interference (ISI), intercarrier interference (ICI) and noise. New channel equalization algorithms are proposed based on the derived analytical signal model. Numerical results show that the derived model matches the simulation results precisely, and the proposed equalization algorithms improve the UFMC system performance in terms of bit error rate (BER).

We tackle the problem of theoretical evaluation of the multistage parallel interference cancellation (PIC) scheme in a DS-CDMA system with orthogonal modulation and long scrambling codes. The studied system operates on the reverse link in a time-varying multipath Rayleigh fading channel. By applying the central limit theorem to multiple access interference (MAI) and intersymbol interference (ISI), as well as identically distributed chips from a single interferer, the bit error rate (BER) performance of the PIC scheme at any stage can be recursively computed from the signal-to-noise ratio, number of users, the number of paths per user, processing gain of the CDMA system, as well as the average received power of each path. The proposed approximative analysis is validated by Monte-Carlo simulations and proved to be accurate, and it gives insight into the performance and capacity one can expect from the PIC based receivers under different situations.

This work investigates a sparse code multiple access (SCMA) assisted multiple unmanned aerial vehicles (UAVs) downlink communication network for improved data services to multiple ground users. Our objective is to maximize the sum-rate of the multi-UAV SCMA network by optimizing the SCMA factor graph matrix used for resource allocation, considering the inter-UAV and intra-UAV interference components. The formulated problem is non-convex in nature and is subject to the SCMA code-book constraints. We propose a factor graph matrix assignment algorithm to solve this optimization problem. Our simulation results demonstrate the superiority of the proposed scheme in terms of rate performance over the benchmark schemes. Thus, compared with orthogonal multiple access strategies, SCMA emerge as a promising candidate for next generation multiple access (NGMA) techniques.

Wireless backhaul is an economical and flexible alternative to wired backhaul, however, it experiences impairments. This research proposes a new secure heterogeneous system model with unreliable wireless backhaul and imperfect channel estimation over Nakagami-m fading channel. To improve the system secrecy performance, we propose three small-cell transmitter selection schemes under channel estimation imperfections and wireless backhaul impairments, namely Optimum Selection (OS), Sub-optimum Selection (SS) and Minimum-Eavesdropping Selection (MES). Novel closed-form expressions of secrecy outage probability (SOP) are derived for these three selection schemes in both practical and ideal scenarios. The novel theoretical analysis and simulation results show the impact of wireless backhaul uncertainties and channel estimation errors on system secrecy performance. In addition, we investigate how the number of small-cell transmitters affects the system secrecy performance. The asymptotic behaviour is provided to obtain insights into the system performance. The analytical derivations and asymptotic expressions are validated by Monte Carlo simulations. Our novel theoretical analysis can guide different physical layer security (PLS) designs.

Multi-service system is an enabler to flexibly support diverse communication requirements for the next generation wireless communications. In such a system, multiple types of services co-exist in one baseband system with each service having its optimal frame structure and low out of band emission (OoBE) waveforms operating on the service frequency band to reduce the inter-service-band-interference (ISvcBI). In this article, a framework for multi-service system is established and the challenges and possible solutions are studied. The multi-service system implementation in both time and frequency domain is discussed. Two representative subband filtered multicarrier (SFMC) waveforms: filtered orthogonal frequency division multiplexing (F-OFDM) and universal filtered multi-carrier (UFMC) are considered in this article. Specifically, the design methodology, criteria, orthogonality conditions and prospective application scenarios in the context of 5G are discussed. We consider both single-rate (SR) and multi-rate (MR) signal processing methods. Compared with the SR system, the MR system has significantly reduced computational complexity at the expense of performance loss due to inter-subband-interference (ISubBI) in MR systems. The ISvcBI and ISubBI in MR systems are investigated with proposed low-complexity interference cancelation algorithms to enable the multi-service operation in low interference level conditions.

Sparse code multiple access (SCMA) building upon orthogonal frequency division multiplexing (OFDM) is a promising wireless technology for supporting massive connectivity in future machine-type communication networks. However, the sensitivity of OFDM to carrier frequency offset (CFO) poses a major challenge because it leads to orthogonality loss and incurs intercarrier interference (ICI). In this paper, we investigate the bit error rate (BER) performance of SCMA-OFDM systems in the presence of CFO over both Gaussian and multipath Rayleigh fading channels. We first model the ICI in SCMA-OFDM as Gaussian variables conditioned on a single channel realization for fading channels. The BER is then evaluated by averaging over all codeword pairs considering the fading statistics. Through simulations, we validate the accuracy of our BER analysis and reveal that there is a significant BER degradation for SCMA-OFDM systems when the normalized CFO exceeds 0.02. Index Terms—Sparse code multiple access (SCMA), orthogonal frequency division multiplexing (OFDM), carrier frequency offset (CFO), bit error rate (BER)

The conventional transmit diversity schemes, such as Alamouti scheme, use several radio frequency (RF) chains to transmit signals simultaneously from multiple antennas. In this paper, we propose a low-complexity repetition time-switched (RTSTD) transmit diversity algorithm, which employs only one RF chain as well as a low-complexity switch for transmission. A mathematical model is developed to assess the performance of the proposed scheme. In order to make it applicable for practical applications, we also investigate its joint application with orthogonal frequency division multiplexing (OFDM) and channel coding techniques to combat frequency selective fading. © 2011 IEEE.

Broadband fixed wireless access (BFWA) is an ideal solution for providing high data rate communications where traditional landlines are either unavailable or too costly to be installed. In this paper we consider a number of alternative techniques to achieve high data rate and high quality of services requirements in these systems, including orthogonal frequency division multiplexing (OFDM), turbo equalization as well as multiple-input multiple-output (MIMO) techniques. In particular, the frequency domain OFDM scheme and time domain turbo equalization will be studied and compared in a MIMO BFWA context, in an attempt to provide some guidelines on how to design high data rate BFWA applications.

This contribution introduces the development of an intelligent monitoring and control framework for chemical processes, integrating the advantages of Industry 4.0 technologies, cooperative control and fault detection via wireless sensor networks. Using information on the process’ structure and behaviour, equipment information, and expert knowledge, the system is able to detect faults. The integration with the monitoring system facilitates the detection and optimises the controller’s actions. The results indicate that the proposed approach achieves high fault detection accuracy based on plant measurements, while the cooperative controllers improve the control of the process.

In this letter, we first incorporate the concept of index modulation (IM) into simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided non-orthogonal multiple access (NOMA) system to improve the spectral efficiency. Specifically, the proposed IM aided STAR-RIS-NOMA system enables extra information bits to be transmitted by allocating subsurfaces to different users in a pre-defined subsurface allocation pattern. Furthermore, an approximate closed form expression on bit error rate (BER) is derived. Simulation results demonstrate that the proposed IM aided STAR-RIS-NOMA system is able to acquire transmission rate improvement compared to the conventional STAR-RIS NOMA.

State-of-the-art channel coding schemes promise data rates close to the wireless channel capacity. However, efficient link adaptation techniques are required in order to deliver such throughputs in practice. Traditional rate adaptation schemes, which are reactive and try to “predict” the transmission mode that maximizes throughput based on “transmission quality indicators”, can be highly inefficient in an evolving wireless ecosystem where transmission can become increasingly dynamic and unpredictable. In such scenarios, “rateless” link adaptation can be highly beneficial. Here, we compare popular rateless approaches in terms of gains and practicality in both traditional and more challenging operating scenarios. We also discuss challenges that need to be addressed to make such systems practical for future wireless communication systems.

In this paper, we first provide a theoretical validation for a low-complexity transmit diversity algorithm which employs only one RF chain and a low-complexity switch for transmission. Our theoretical analysis is compared to the simulation results and proved to be accurate. We then apply the transmit diversity scheme to multiple-input and multiple-output (MIMO) systems with bit-interleaved coded modulation (BICM). © 2012 IEEE.

A novel Multiple-Input and Multiple- Output (MIMO) transmission scheme termed as Space- Time Block Coded Quadrature Spatial Modulation (STBC-QSM) is proposed. It amalgamates the concept of Quadrature Spatial Modulation (QSM) and Space- Time Block Coding (STBC) to exploit the diversity benefits of STBC relying on sparse Radio Frequency (RF) chains. In the proposed STBC-QSM scheme, the conventional constellation points of the STBC structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by conventional STBC blocks. Furthermore, an efficient Bayesian Compressive Sensing (BCS) algorithm is developed for our proposed STBCQSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes. Moreover, the proposed BCS detector is capable of approaching the Maximum Likelihood (ML) detector’s performance despite only imposing a complexity near similar to that of the Minimum Mean Square Error (MMSE) detector in the high Signal to Noise Ratio (SNR) regions.

A novel high-isolation, monostatic, circularly polarized (CP) simultaneous transmit and receive (STAR) array dielectric resonator antenna (DRA) is presented. The proposed in-band full-duplex (IBFD) CP DRA system consists of 32 identical elements for each transmit and receiver part. Each element includes two rectangular dielectric resonators with different permittivity of 5 and 10 excited by a vertical strip connected to the microstrip line at the backplane. A stub connected to the ground plane is added between Rx and Tx to improve the isolation. In addition, an inverted U-shaped parasitic strip is carefully placed between two feeding networks to further enhance the TX/RX isolation. The measured results exhibit high TX/RX isolation of more than 50 dB over the desired operating bandwidth from 4.8 GHz to 4.95 GHz with a high total efficiency greater than 85% and a peak gain of about 18.7 dBi for both Port 1 and Port 2.

In Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radio systems, power optimization algorithms have been evaluated to maximize the achievable data rates of the Secondary User (SU). However, unrealistic assumptions are made in the existing work, i.e. a Gaussian input distribution and traditional interference model that assumes frequency division multiplexing modulated Primary User (PU) with perfect synchronization between the PU and the SU. In this paper, we first derive a practical interference model by assuming OFDM modulated PU with imperfect synchronization. Based on the new interference model, the power optimization problem is proposed for the Finite Symbol Alphabet (FSA) input distribution (i.e., M-QAM), as used in practical systems. The proposed scheme is shown to save transmit power and to achieve higher data rates compared to the Gaussian optimized power allocation and the uniform power loading schemes. Furthermore, a theoretical framework is established in this paper to estimate the power saving by evaluating optimal power allocation for the Gaussian and the FSA input. Our theoretical analysis is verified by simulations and proved to be accurate. It provides guidance for the system design and gives deeper insights into the choice of parameters affecting power saving and rate improvement.

This paper proposes a novel transmission policy for an intelligent reflecting surface (IRS) assisted wireless powered sensor network (WPSN). An IRS is deployed to enhance the performance of wireless energy transfer (WET) and wireless information transfer (WIT) by intelligently adjusting phase shifts of each reflecting elements. To achieve its self-sustainability, the IRS needs to collect energy from the ES to support its control circuit operation. Our proposed policy for the considered system is called IRS assisted harvest-then-transmit time switching (IRS-HTT-TS) which schedules the transmission time slots by switching between energy collection and energy reflection modes. We study the performance of the proposed transmission policy in terms of the achievable sum throughput, and investigate a joint design of the transmission time slots, the power allocation, as well as the discrete phase shifts of the WET and WIT. This formulates the problem as a mixed-integer non-linear program (MINLP), which is NP-hard and non-convex. To deal with this problem, we first relax it to the one with continuous phase shifts. Consequently, we propose a two-step approach and decompose the original problem into two sub-problem, each being solved separately. Specifically, we independently solve the first sub-problem with respect to the phase shifts of the WIT in terms of closed-form expression. Then, we consider two cases to solve the second sub- problem. For the special case without the circuit power of each sensor node, the Lagrange dual method and the Karush-Kuhn- Tucker (KKT) conditions are applied to derive the optimal closed- form transmission time slots, power allocation, and phase shift of the WET. Moreover, we exploit the second sub-problem for the general case with the circuit power of each sensor node, which can be solved via employing a semi-definite programming (SDP) relaxation.

5G New Radio (NR) is touted as a pivotal enabling technology for the genuine realization of connected and cooperative autonomous driving. Despite numerous research efforts in recent years, a systematic overview on the role of 5G NR in future connected autonomous communication networks is missing. To fill this gap and to spark more future research, this paper introduces the technology components of 5G NR and discusses the evolution from existing cellular vehicle-to-everything (V2X) technology towards NR-V2X. We primarily focus on the key features and functionalities of physical layer, Sidelink communication and its resource allocation, architecture flexibility, security and privacy mechanisms, and precise positioning techniques. Moreover, we envisage and highlight the potential of machine learning for further performance enhancement in NR-V2X services. Lastly, we show how 5G NR can be configured to support advanced V2X use cases.

Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future internet-of-things (IoT) users due to its ultra-wide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input multipleoutput (MIMO) represent two effective solutions for compensating the large pathloss in THz systems. In this paper, we consider an IRS-aided multi-user THz MIMO system with orthogonal frequency division multiple access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated non-convex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

In this paper, a high-gain phased array antenna with wide-angle beam-scanning capability is proposed for fifth- generation (5G) millimeter-wave applications. First, a novel, end-fire, dual-port antenna element with dual functionalities of radiator and power splitter is designed. The element is composed a substrate integrated cavity (SIC) and a dipole based on it. The resonant frequencies of the SIC and dipole can be independently tuned to broaden the impedance bandwidth. Based on this dual-port element, a 4-element subarray can be easily constructed without resorting to a complicated feeding network. The end-fire subarray features broad beam-width of over 180 degrees, high isolation, and low profile, rendering it suitable for wide-angle beam-scanning applications in the H-plane. In addition, the methods of steering the radiation pattern downwards or upwards in the E-plane are investigated. As a proof-of-concept, two phased array antennas each consisting of eight subarrays are designed and fabricated to achieve the broadside and wide-angle beam-scanning radiation. Thanks to the elimination of surface wave, the mutual coupling between the subarrays can be reduced for improving the scanning angle while suppressing the side-lobe level. The experimental predictions are validated by measurement results, showing that the beam of the antenna can be scanned up to 65 degrees with a scanning loss only 3.7 dB and grating lobe less than -15 dB.

In this paper, a novel terahertz (THz) spectroscopy technique and a new graphene-based sensor is proposed. The proposed sensor consists of a graphene-based metasurface (MS) that operates in reflection mode over a broad range of frequency band (0.2 -6 THz) and can detect relative permittivity of up to 4 with a resolution of 0.1 and a thickness ranging from 5 μm to 600 μm with a resolution of 0.5 μm. To the best of author’s knowledge, such a THz sensor with such capabilities has not been reported yet. Additionally, an equivalent circuit of the novel unit cell is derived and compared with two conventional grooved structures to showcase the superiority of the proposed unit cell. The proposed spectroscopy technique utilizes some unique spectral features of a broadband reflection wave including Accumulated Spectral power (ASP) and Averaged Group Delay (AGD), which are independent to resonance frequencies and can operate over a broad range of spectrum. ASP and AGD can be combined to analyse the magnitude and phase of the reflection diagram as a coherent technique for sensing purposes. This enables the capability to distinguish between different analytes with high precision which, to the best of author’s knowledge, has been accomplished for the first time.

Orthogonal Frequency Division Multiple Access (OFDMA) as well as other orthogonal multiple access techniques fail to achieve the system capacity limit in the uplink due to the exclusivity in resource allocation. This issue is more prominent when fairness among the users is considered in the system. Current Non-Orthogonal Multiple Access techniques (NOMA) introduce redundancy by coding/spreading to facilitate the users' signals separation at the receiver, which degrade the system spectral efficiency. Hence, in order to achieve higher capacity, more efficient NOMA schemes need to be developed. In this paper, we propose a NOMA scheme for uplink that removes the resource allocation exclusivity and allows more than one user to share the same subcarrier without any coding/spreading redundancy. Joint processing is implemented at the receiver to detect the users' signals. However, to control the receiver complexity, an upper limit on the number of users per subcarrier needs to be imposed. In addition, a novel subcarrier and power allocation algorithm is proposed for the new NOMA scheme that maximizes the users' sum-rate. The link-level performance evaluation has shown that the proposed scheme achieves bit error rate close to the single-user case. Numerical results show that the proposed NOMA scheme can significantly improve the system performance in terms of spectral efficiency and fairness comparing to OFDMA.

Employing multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has emerged as a powerful transceiver strategy for multi-antenna networks. In this paper, we design RS precoders for an overloaded multicarrier multigroup multicast downlink system, and analyse the error performance. RS splits each group message into degraded and designated parts. The degraded parts are combined and encoded into a degraded stream, while the designated parts are encoded in designated streams. All streams are precoded and superimposed in a non-orthogonal fashion before being transmitted over the same time-frequency resource. We first derive the optimized RS-based precoder, where the design philosophy is to achieve a fair user group rate for the considered scenario by solving a joint max-min fairness and sum subcarrier rate optimization problem. Comparing with other precoding schemes including the state-of-the-art multicast transmission scheme, we show that the RS precoder outperforms its counterparts in terms of the fairness rate, with Gaussian signalling, i.e., idealistic assumptions. Then we integrate the optimized RS precoder into a practical transceiver design for link-level simulations (LLS), with realistic assumptions such as finite alphabet inputs and finite code block length. The performance metric becomes the coded bit error rate (BER). In the system under study, low-density parity-check (LDPC) encoding is applied at the transmitter, and iterative soft-input soft-output detection and decoding are employed at the successive interference cancellation based receiver, which completes the LLS processing chain and helps to generate the coded error performance results which validate the effectiveness of the proposed RS precoding scheme compared with benchmark schemes, in terms of the error performance. More importantly, we unveil the corresponding relations between the achievable rate in the idealistic case and coded BER in the realistic case, e.g., with finite alphabet input, for the RS precoded multicarrier multigroup multicast scenario. Index Terms—Downlink multiuser MISO, multicarrier multi-group multicast, rate-splitting, optimization, coded bit error rate BER.

Sparse code multiple access (SCMA) is a promising air interface candidate technique for next generation mobile networks, especially for massive machine type communications (mMTC). In this paper, we design a LDPC coded SCMA detector by combining the sparse graphs of LDPC and SCMA into one joint sparse graph (JSG). In our proposed scheme, SCMA sparse graph (SSG) defined by small size indicator matrix is utilized to construct the JSG, which is termed as sub-graph based joint sparse graph of SCMA (SG-JSG-SCMA). In this paper, we first study the binary-LDPC (B-LDPC) coded SGJSG- SCMA system. To combine the SCMA variable node (SVN) and LDPC variable node (LVN) into one joint variable node (JVN), a non-binary LDPC (NB-LDPC) coded SG-JSG-SCMA is also proposed. Furthermore, to reduce the complexity of NBLDPC coded SG-JSG-SCMA, a joint trellis representation (JTR) is introduced to represent the search space of NB-LDPC coded SG-JSG-SCMA. Based on JTR, a low complexity joint trellis based detection and decoding (JTDD) algorithm is proposed to reduce the computational complexity of NB-LDPC coded SGJSG- SCMA system. According to the simulation results, SG-JSGSCMA brings significant performance improvement compare to the conventional receiver using the disjoint approach, and it can also outperform a Turbo-structured receiver with comparable complexity. Moreover, the joint approach also has advantages in terms of processing latency compare to the Turbo approaches.

The Long Term Evolution (LTE) may provide ubiquitous mobile broadband services with all IP architecture, however, the quality of service (QoS) of LTE systems is seriously affected by the network congestions, packet losses, jitters, latencies and other QoS issues in all IP networks. Thus it is valuable to investigate and design efficient resource scheduling algorithms to improve the performance of data services and end-user experiences. In this paper we propose an improved radio resource scheduling algorithm over the existing semi-continuous scheduling algorithm for the voice over Internet Protocol (VoIP) data packets. Through mapping the TCP (transmission control protocol) ACK (acknowledgement) packets into a higher priority logical channel, the probability of both the discarded ACK packets and congestions in the wireless channels are reduced. As the result, the scheme may avoid frequently opening the TCP congestion control mechanism. The simulation results have shown the advantages of our proposed algorithm, such as the RTT (Round-Trip Time) packet delay reduction, improved throughput, acceptable stability, desirable performance, and son on © 2013 IEEE.

Simultaneous improvement of matching and isolation for a modified two-element microstrip patch antenna array is proposed. Two simple patch antennas in a linear array structure are designed, whereas, the impedance matching and isolation are improved without using any conventional matching networks. The presented low profile multifunctional via-less structure comprises of only two narrow T-shaped stubs connected to feed lines, a narrow rectangular stub between them, and a narrow rectangular slot on the ground plane. This design provides a simple, compact structure with low mutual coupling, low cost and no adverse effects on the radiation and resonance. To validate the design, a compact very-closely-spaced antenna array prototype is fabricated at 5.5 GHz which is suitable for multiple-input-multiple-output (MIMO) systems. The measured and simulated results are in good agreement with a 16 dB, and 40 dB of improvements in the matching and isolation, respectively.

—A specific limitation of spatial modulation (SM) is that the number of transmit antennas must be a power of two, otherwise it will cause a fractional bits problem. To solve this problem, this paper proposes a novel transmission scheme, called golden angle modulation aided fractional spatial modulation (GAM-FSM), which exploits the property of golden angle modulation (GAM), i.e., it can have an arbitrary number of constellation points or the modulation order of GAM can be any positive integer. In addition, the average bit error probability (ABEP) and mutual information (MI) of our proposed GAM-FSM scheme are derived. To further enhance the system performance , geometric and probabilistic constellation shaping aided GAM-FSM schemes are investigated and optimized under three optimization criteria, maximization of the minimum Euclidean distance (MMED), minimization of the the bit error rate (MBER) and maximization of the MI (MMI). Simulation results reveals the superiority of our proposed GAM-FSM over the conventional fractional spatial modulation (FSM) systems. Besides, simulation results also show that our proposed constellation shaping aided GAM-FSM schemes exhibit significant system performance improvements compared to the one without constellation shaping. Index Terms—MIMO, fractional spatial modulation (FSM), golden angle modulation (GAM), average bit error probability (ABEP), mutual information (MI), geometric and probabilistic constellation shaping.

This paper introduces a millimeter-wave multipleinput- multiple-output (MIMO) antenna for autonomous (selfdriving) cars. The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view. By using four antennas of this kind on four corners of the car’s bumper, it is possible to have a full 360 deg view around the car. The designed antenna is simulated by two commercially full-wave packages and the results indicate that the proposed method can successfully bring the required 90 deg angle of view.

This work addresses joint transceiver optimization for multiple-input, multiple-output (MIMO) systems. In practical systems the complete knowledge of channel state information (CSI) is hardly available at transmitter. To tackle this problem, we resort to the codebook approach to precoding design, where the receiver selects a precoding matrix from a finite set of pre-defined precoding matrices based on the instantaneous channel condition and delivers the index of the chosen precoding matrix to the transmitter via a bandwidth-constraint feedback channel. We show that, when the symbol constellation is improper, the joint codebook based precoding and equalization can be designed accordingly to achieve improved performance compared to the conventional system.

In this paper, we propose a novel linear transmit precoding strategy for multiple-input, multiple-output (MIMO) systems employing improper signal constellations. In particular, improved zero-forcing (ZF) and minimum mean square error (MMSE) precoders are derived based on modified cost functions, and are shown to achieve a superior performance without loss of spectrum efficiency compared to the conventional linear and nonlinear precoders. The superiority of the proposed precoders over the conventional solutions are verified by both simulation and analytical results. The novel approach to precoding design is also applied to the case of an imperfect channel estimate with a known error covariance as well as to the multi-user scenario where precoding based on the nullspace of channel transmission matrix is employed to decouple multi-user channels. In both cases, the improved precoding schemes yield significant performance gain compared to the conventional counterparts.

A machine learning (ML) technique has been used to synthesis a linear millimetre wave (mmWave) phased array antenna by considering the phase-only synthesis approach. For the first time, gradient boosting tree (GBT) is applied to estimate the phase values of a 16-element array antenna to generate different far-field radiation patterns. GBT predicts phases while the amplitude values have been equally set to generate different beam patterns for various 5G mmWave transmission scenarios such as multicast, unicast, broadcast and unmanned aerial vehicle (UAV) applications.

We treat the problems of propagation delay and channel estimation as well as data detection of orthogonally modulated signals in an asynchronous DS-CDMA system over fading channels using the maximum likelihood (ML) approach. The overwhelming computational complexity of the ML algorithm makes it unfeasible for implementation. The emphasis of this paper is to reduce its complexity by some approximation methods. The derived approximative ML schemes are compared with conventional algorithms as well as some others, e.g., the parallel interference cancellation (PIC) for data detection and the subspace-based algorithm for acquisition

Network slicing has been identified as one of the most important features for 5G and beyond to enable operators to utilize networks on an as-a-service basis and meet the wide range of use cases. In physical layer, the frequency and time resources are split into slices to cater for the services with individual optimal designs, resulting in services/slices having different baseband numerologies (e.g., subcarrier spacing) and / or radio frequency (RF) front-end configurations. In such a system, the multi-service signal multiplexing and isolation among the service/slices are critical for the Physical-Layer Network Slicing (PNS) since orthogonality is destroyed and significant inter-service/ slice-band-interference (ISBI) may be generated. In this paper, we first categorize four PNS cases according to the baseband and RF configurations among the slices. The system model is established by considering a low out of band emission (OoBE) waveform operating in the service/slice frequency band to mitigate the ISBI. The desired signal and interference for the two slices are derived. Consequently, one-tap channel equalization algorithms are proposed based on the derived model. The developed system models establish a framework for further interference analysis, ISBI cancelation algorithms, system design and parameter selection (e.g., guard band), to enable spectrum efficient network slicing.

We propose channel estimation algorithms and pilot signal optimization for the universal filtered multi-carrier (UFMC) system based on the comb-type pilot pattern. By considering the least square linear interpolation (LSLI), discrete Fourier transform (DFT), minimum mean square error (MMSE) and relaxed MMSE (RMMSE) channel estimators, we formulate the pilot signals optimization problem by minimizing the estimation MSE subject to the power constraint on pilot tones. The closed-form optimal solutions and minimum MSE are derived for LSLI, DFT, MMSE and RMMSE estimators.

Multicarrier-low density spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near optimum multiuser detection. In MC-LDSMA, each user’s symbol spread on a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging comparing to some well-known multiple access techniques. In this paper, we study the radio resource allocation for single-cell MC-LDSMA system. Firstly, we consider the single-user case, and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple access channel, we provide an optimal solution for MC-LDSMA that maximizes the users’ weighted sum-rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with OFDMA.

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

This work addresses joint transceiver optimization for multiple-input, multiple-output (MIMO) systems. In practical systems the complete knowledge of channel state information (CSI) is hardly available at transmitter. To tackle this problem, we resort to the codebook approach to precoding design, where the receiver selects a precoding matrix from a finite set of pre-defined precoding matrices based on the instantaneous channel condition and delivers the index of the chosen precoding matrix to the transmitter via a bandwidth-constraint feedback channel. We show that, when the symbol constellation is improper, the joint codebook based precoding and equalization can be designed accordingly to achieve improved performance compared to the conventional system. © 2012 IEEE.

In this paper, a single-layer planar antenna with vertical polarization and omni-directional radiation is proposed for wearable applications. The antenna consists of two identical shorted patches which are face-to-face located and fed by a microstrip line at the center. Due to the structural symmetry, the current distribution and electric-field distribution are symmetrical regarding the feed, which result in vertical linear polarization normal to the antenna and omni-directional radiation pattern in the azimuthal plane. To verify the design concept, an antenna prototype operating at 2.45 GHz is designed, fabricated and tested. Measured results concur well with the simulations, showing that the antenna has a good impedance matching, omnidirectional radiation pattern, and vertical polarization in the band of interest. The proposed antenna can be a good candidate for wearable and other wireless communication systems.

In this letter, the performance bound of the IEEE 802.16d channel is examined analytically in order to gain an insight into its theoretical potential. Different design strategies, such as orthogonal frequency division multiplexing (OFDM) and single-carrier frequency-domain equalization (SC-FDE), time-domain decision feedback equalization (DFE), and sphere decoder (SD) techniques are discussed and compared to the theoretical bound.

In this paper, an ultra-wideband, Dielectric Resonator Antenna (DRA) has been proposed. The proposed antenna is based on isosceles triangular DRA (TDRA), which is fed from the base side using a 50Ω probe. For bandwidth enhancement and radiation characteristics improvement, a partially cylindrical-shape hole is etched from its base side which approached probe feed to the center of TDRA. The dielectric resonator (DR) is located over an extended conducting ground plane. This technique has significantly enhanced antennas bandwidth from 48.8% to 80% (5.29-12.35 GHz), while the biggest problem was radiation characteristics. The basis antenna possesses negative gain in a wide range of bandwidth from 7.5 GHz to 10.5 GHz down to -13.8 dBi. Using this technique improve antenna gain over 1.6 dBi for whole bandwidth, while peak gain is 7.2 dBi.

We present a simple transmit diversity technique, called repetition time-switched transmit diversity (R-TSTD), which is a modified version of the well-known time-switched transmit diversity (TSTD) algorithm. Throughout the paper, we focus on the scenario of a transmitter with n = 2 antennas. The idea behind R-TSTD is to use only one antenna at a time while still ensuring that all constellation symbols are transmitted via both antennas. Thus, unlike the classical TSTD technique, R-TSTD does provide a transmit diversity effect similar to that achieved with space-time coding (STC) algorithms. The error performance of the proposed R-TSTD system is compared to that of the Alamouti STC scheme via Monte Carlo computer simulations. It is shown that, in the absence of any outer error-correcting code, Alamouti STC slightly outperforms R-TSTD. However, when a near-capacity channel code is employed as an outer code, the error performance achieved using R-TSTD is significantly better than that obtained with Alamouti STC, provided that the desired spectral efficiency is sufficiently low. © 2011 National Institute of Inform.

In this paper, we study an enhanced subspace based approach for the mitigation of multiple access interference (MAI) in direct-sequence code-division multiple-access (DS-CDMA) systems over frequency-selective channels. Blind multiuser detection based on signal subspace estimation is of special interest in mitigating MAI in CDMA systems since it is impractical to assume perfect knowledge of parameters such as spreading codes, time delays and amplitudes of all the users in a rapidly changing mobile environment. We develop a new blind multiuser detection scheme which only needs the priori knowledge of the signature waveform and timing of the user of interest. By exploiting the improper nature of multiple access interference (MAI) and intersymbol interference (ISI), the enhanced detector shows clear superiority to the conventional subspace-based blind multiuser detector. The performance advantages are shown to be more obvious in heavily loaded systems when the number of active users is large. © 2011 IEEE.

This work presents a resource management framework for optimizing the sum-rate in a sparse code multiple access (SCMA)-assisted UAV downlink system. We formulate two optimization problems for maximizing the overall sum-rate: the first problem addresses UAV 3D deployment and trajectory optimization with energy constraints, while the second focuses on optimizing SCMA subcarrier and power allocation optimization , subject to factor graph matrix (FGM) constraints and a minimum user data rate. Since the optimization problems are non-convex, the complexity of finding the global optimal solutions is prohibitive. We propose a gradient ascent-based iterative algorithm to compute the optimal UAV 3D deployment and trajectory. Further, an effective channel state information-based algorithm is proposed for FGM assignment, followed by a Lagrange dual decomposition method to solve the power allocation problem efficiently. Our research findings demonstrate that the optimization of the UAV trajectory gives improved sum-rate within the specified energy budget. Further, employing CSI-based multiple subcarrier allocation and strategic power allocation can significantly improve system performance compared to the benchmark schemes.

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

In this letter, we incorporate index modulation (IM) into affine frequency division multiplexing (AFDM), called AFDM-IM, to enhance the bit error rate (BER) and energy efficiency (EE) performance. In this scheme, the information bits are conveyed not only by M-ary constellation symbols, but also by the activation of the chirp subcarriers (SCs) indices, which are determined based on the incoming bit streams. Then, two power allocation strategies, namely power reallocation (PR) strategy and power saving (PS) strategy, are proposed to enhance BER and EE performance, respectively. Furthermore, the average bit error probability (ABEP) is theoretically analyzed. Simulation results demonstrate that the proposed AFDM-IM scheme achieves better BER performance than the conventional AFDM scheme.

In this paper, we present different linear and nonlinear iterative data detection schemes for the asynchronous direct-sequence code-division multiple access (DS-CDMA) systems employing orthogonal signalling formats and long scrambling codes. Compared to the conventional receiver and other noncoherent multiuser detectors, coherent multiuser detection schemes achieve much better performance provided that the channels are accurately estimated. To this end, we proposed several channel estimation algorithms to estimate multipath Rayleigh fading channels. Different data detection and channel estimation schemes are compared in terms of BER performance. Based on the numerical results, some recommendations are made on how to choose multiuser detectors and channel estimation algorithms in practical CDMA systems.

In this paper, we aim at solving the problem of joint delay estimation and data detection of the orthogonal modulated signals in the asynchronous DS-CDMA system employing aperiodic long spreading codes over fading channels. The general system requirement of low error rate in data demodulation necessitates the reliable synchronization mechanisms. Synchronization of CDMA signals with long spreading codes is a more challenging task than that of CDMA signals with short spreading codes. In this work, two algorithms are introduced to perform acquisition and tracking of orthogonal modulated signals with long spreading codes, followed by data detection. The numerical results show that when applying to the asynchronous system with random propagation delays, the proposed algorithms approximate the performance that is attainable in the synchronized and chip-aligned system.

This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.

Non-terrestrial networks (NTNs) will become an indispensable part of future wireless networks. Integration with terrestrial networks will provide new opportunities for both satellite and terrestrial telecommunication industries and therefore there is a need to harmonize them in a unified technological framework. Among different NTNs, low earth orbit (LEO) satellites have gained increasing attention in recent years and several companies have filed federal communication commission (FCC) proposals to deploy their LEO constellation in space. This is mainly due to several desired features such as large capacity and low latency. In addition, recent successful LEO network deployments such as Starlink have motivated other companies. In the past satellite and terrestrial wireless networks have been evolving separately but now they are joining forces to enhance coverage and connectivity experience in the future wireless networks. The 3rd Generation Partnership Project (3GPP) is one of the dominating standardization bodies that is working on various technical aspects to provide ubiquitous access to the 5G networks with the aid of NTNs. Initial steps have been taken to adopt 5G state of the art technologies and concepts and harmonized them with the conditions met in non-terrestrial networks. In this article, we review some of the important technical considerations in 5G NTNs with emphasis on the radio access network (RAN) part and provide some simulation based results to assess the required modifications and shed light on the design considerations

—This paper presents a 2-bit unit cell for reconfigurable intelligent surface (RIS) applications of beamforming and beam-steering in the frequency range 2 (FR2) from the fifth generation of mobile communications (5G). The proposed RIS unit cell is based on a printed split-ring resonator (SRR) loaded with a varactor diode, which connects two circular loops at the top and a ground plane at the bottom, resulting in a 0.245x0.245λ0 total area. The entire unit cell element encompasses four conducting layers, in which the first two ones form the SRR, whereas RF chokes are printed at the middle layer to isolate the DC circuit and the bias lines are routed at the fourth layer. The RIS unit cell design has been conceived using the full-wave electromagnetic solver ANSYS HFSS. Its numerical results demonstrate reflection phase shift up to 270º and reflection magnitude higher than 0.5 at 24.5 GHz. The proposed reconfigurable intelligent surface might be applied to future wireless communication systems, planar antenna reflectors, and vortex beam generation. Index Terms—beamforming, beam steering, metamaterial and reconfigurable intelligent surface.

—In-band full-duplex (IBFD) has the potential to not only double the spectral efficiency (SE) but also greatly reduce the transmission latency. However, the less maturity of existing self-interference cancellation (SIC) techniques renders IBFD impractical for future wireless applications. To inherit the merits of full-duplex (FD) but with practical SIC requirements, multicarrier-division duplex (MDD) was proposed and studied recently. In this article, we demonstrate the advantages of MDD over the IBFD mode and the conventional half-duplex (HD) modes of frequency-division duplex (FDD) and time-division duplex (TDD) from several essential aspects, including SIC capability, resource integration and the support for high-mobility communications. Several numerical results are included to show that MDD outperforms IBFD in terms of energy efficiency and SIC. Compared with the HD modes, MDD is capable of efficiently integrating the DL/UL resources to achieve higher SE and significantly outperforms TDD, when communicating over fast time-varying channels. Lastly, some implementation challenges of MDD systems are discussed.

—In this work, we study a simultaneous transmitting and reflecting reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output network. For the system under consideration, we maximize the weighted sum rate, mainly based on the energy splitting (ES) scheme. To tackle this optimization problem, a sub-optimal block coordinate descent (BCD) algorithm is proposed to design the precoding matrices and the transmitting and reflecting coefficients (TRCs) in an alternate manner. Specifically, the precoding matrices are solved using the Lagrange dual method, while the TRCs are obtained using the constrained concave-convex procedure (CCCP). The simulation results reveal that: 1) Simultaneous transmitting and reflecting RIS (STAR-RIS) can achieve better performance than conventional reflecting/transmiting-only RIS; 2) In unicast communication , time switching (TS) scheme outperforms the ES and mode selection (MS) schemes, while in broadcast communication, ES scheme outperforms the TS and MS schemes.

In this paper, we explore a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted integrated sensing and communication (ISAC) secure communication system. The average long-term security rate of the legitimate user (LU) is maximized by jointly designing the receive filters and transmit beamforming of the base station (BS), and the transmitting and reflecting coefficients of STAR-RIS, and in the meantime, guaranteeing the lower bound of echo signal-to-noise ratio (SNR) and the achievable rate of LU constraint. We propose to apply two deep reinforcement learning (DRL) algorithms to solve the complex non-convex problem and maximize the long-term benefits of the system by optimizing the BS beamforming and STAR-RIS phase shifts. The simulation results thoroughly evaluate the performance of two DRL algorithms and demonstrate that STAR-RIS outperforms the conventional reconfigurable intelligent surface (RIS) in comparison with two benchmarks. Index Terms—integrated sensing and communication (ISAC), deep reinforcement learning (DRL), simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), secrecy rate

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. First, cluster heads are selected for each beam, and analog precoding based on discrete phase is designed. Then, users are clustered based on channel correlation, and NOMA technology is employed to serve the users. In addition, a low-complexity forced-zero method is utilized to design digital precoding in order to eliminate inter-cluster interference. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS subject to the total transmit power budget, minimal achievable rate requirement of each user, and IRS reflection coefficients. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results verify the convergence of the proposed algorithm, and deploying IRS can bring significant beamforming gains to suppress the eavesdropping.

Decentralized dynamic spectrum allocation (DSA) that exploit adaptive antenna array interference mitigation (IM) diversity at the receiver, is studied for interference-limited environments with high level of frequency reuse. The system consists of base stations (BSs) that can optimize uplink frequency allocation to their user equipments (UEs) to minimize impact of interference on the useful signal, assuming no control over band allocation of other BSs sharing the same bands. To this end, “good neighbor” (GN) rules allow effective trade off between the equilibrium and transient decentralized DSA behavior if the performance targets are adequate to the interference scenario. In this paper, we extend the GN rules by including a spectrum occupation control that allows adaptive selection of the performance targets corresponding to the potentially “interference free” DSA; define the semi-analytic absorbing Markov chain model for the GN DSA with occupation control and study the convergence properties including effects of possible breaks of the GN rules; and for higher-dimension networks, develop the simplified search GN algorithms with occupation and power control (PC) and demonstrate their efficiency by means of simulations in the scenario with unlimited requested network occupation.

This paper presents details of the indoor wideband and directional propagation measurements at 26 GHz in which a wideband channel sounder using a millimeter wave (mmWave) signal analyzer and vector signal generator was employed. The setup provided 2 GHz bandwidth and the mechanically steerable directional lens antenna with 5 degrees beamwidth provides 5 degrees of directional resolution over the azimuth. Measurements provide path loss, delay and spatial spread of the channel. Angular and delay dispersion are presented for line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios.

In this paper, a high flat gain waveguide-fed aperture antenna has been proposed. For this purpose, two layers of FR4 dielectric as superstrates have been located in front of the aperture to enhance the bandwidth and the gain of the antenna. Moreover, a conductive shield, which is connected to the edges of the ground plane and surrounding aperture and superstrates, applied to the proposed structure to improve its radiation characteristics. The proposed antenna has been simulated with HFSS and optimized with parametric study and the following results have been obtained. The maximum gain of 13.0 dBi and 0.5-dBi gain bandwidth of 25.9 % (8.96 – 11.63 GHz) has been achieved. The 3-dBi gain bandwidth of the proposed antenna is 40.7% (8.07-12.20 GHz), which has a suitable reflection coefficient (≤-10dBi) in whole bandwidth. This antenna comprises a compact size of (1.5λ×1.5λ), easy structure and low-cost fabrication.

Spatial modulation (SM) is a new multiple-input multiple-output (MIMO) paradigm in which only one transmit antenna is activated over every symbol duration. So far, efficient SM training sequences (different from the existing design for conventional MIMO systems) remain largely open. Motivated by this research problem, we introduce a novel class of sequence pairs, called “cross Z-complementary pairs (CZCPs)", each displaying zero-correlation zone (ZCZ) properties for both their aperiodic autocorrelation sums and crosscorrelation sums. A CZCP may be transmitted in two non-orthogonal SM channels and hence proper design should be conducted to minimize the cross-interference of the two constituent sequences. We construct perfect CZCPs based on selected Golay complementary pairs. We show that the training sequences derived from our proposed CZCPs lead to optimal channel estimation performance over frequency-selective SM channels.

We propose a novel turbo detection scheme based on the factor graph serial-schedule belief propagation equalization algorithm with low complexity for single-carrier faster-than-Nyquist (FTN) and multicarrier FTN signaling. In this work, the additive white Gaussian noise channel and multi-path fading channels are both considered. The iterative factor graph-based equalization algorithm can deal with severe intersymbol interference and intercarrier interference introduced by the generation of single-carrier and multi-carrier FTN signals, as well as the effect of multi-path fading. With the application of Gaussian approximation, the complexity of the proposed equalization algorithm is significantly reduced. In the turbo detection, Low density parity check code is employed. The simulation results demonstrate that the factor graph-based turbo detection method can achieve satisfactory performance with low complexity.

Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains to be exploited. In this paper, we commence by overviewing state-of-the-art schemes from the aspects of waveform design and signal processing. Nevertheless, these approaches face the dilemma of mutual compromise between C&S performance. To this end, we reveal and exploit the synergy between C&S. In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieving the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction network (JCASCasterNet). With just a cost-effective and energy-efficient single sensing antenna, the proposed scheme achieves enhanced overall performance while requiring only limited pilot and feedback signaling overhead. In the end, we outline the challenges that lie ahead in the future development of integrated sensing and communication networks, along with promising directions for further research.

This work reports preliminary results and a prototype of an innovative mechanical beam steering circular patch antenna for 5G indoor cellular access networks with sub6 GHz operation. The beam steering is achieved using 18 screws installed around the circular patch radiator, working as a reflector by proper managing the screws position and height. The new antenna can steer its main beam over 360º in azimuth plane and from -30º to 30º in the elevation plane with gain up to 8.01 dBi at 4.6 GHz.

Decentralized dynamic spectrum allocation (DSA) that exploits adaptive antenna array interference mitigation diversity at the receiver, is studied for interference-limited environments with high level of frequency reuse. The system consists of base stations (BSs) that can optimize uplink frequency allocation to their user equipments (UEs) to minimize impact of interference on the useful signal, assuming no control over resource allocation of other BSs sharing the same bands. To this end“, good neighbor” (GN) rules allow effective trade-off between the equilibrium and transient decentralized DSA behavior if the performance targets are adequate to the interference scenario. In this paper, we 1) extend the GN rules by including a spectrum occupation control that allows adaptive selection of the performance targets; 2) derive estimates of absorbing state statistics that allow formulation of applicability areas for different DSA algorithms; 3) define a semi-analytic absorbing Markov chain model and study convergence probabilities and rates of DSA with occupation control including networks with possible partial breaks of the GN rules. For higher-dimension networks, we develop simplified search GN algorithms with occupation and power control and demonstrate their efficiency by means of simulations.

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory quality of service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as hostile wireless channels (e.g., high mobility or asynchronous transmissions), increasingly fragmented and congested spectrum, hardware imperfections, and explosive growth of vehicular communication devices. Therefore, it is highly desirable to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation (RA) schemes for a range of vehicular network technologies including dedicated short range communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

The first 5G (5th generation wireless systems) New Radio Release-15 was recently completed. However, the specification only considers the use of unicast technologies and the extension to point-to-multipoint (PTM) scenarios is not yet considered. To this end, we first present in this work a technical overview of the state-of-the-art LTE (Long Term Evolution) PTM technology, i.e., eMBMS (evolved Multimedia Broadcast Multicast Services), and investigate the physical layer performance via link-level simulations. Then based on the simulation analysis, we discuss potential improvements for the two current eMBMS solutions, i.e., MBSFN (MBMS over Single Frequency Networks) and SCPTM (Single-Cell PTM). This work explicitly focus on equipping the current eMBMS solutions with 5G candidate techniques, e.g., multiple antennas and millimeter wave, and its potentials to meet the requirements of next generation PTM transmissions.

Sparse code multiple access (SCMA) is a promising multiuser communication technique for the enabling of future massive machine-type networks. Unlike existing codebook design schemes assuming uniform power allocation, we present a novel class of SCMA codebooks which display power imbalance among different users for downlink transmission. Based on the Star-QAM mother constellation structure and with the aid of genetic algorithm, we optimize the minimum Euclidean distance (MED) and the minimum product distance (MPD) of the proposed codebooks. Numerical simulation results show that our proposed codebooks lead to significantly improved error rate performances over Gaussian channels and Rayleigh fading channels.

This paper analyses the New Radio (NR) air interface waveforms and numerologies in the context of current activities and studies of 3GPP related to the feasibility and standardisation of necessary adaptations for the 5G NR to support integrated-satellite-terrestrial networks with low earth orbit (LEO) satellites. Frequency-localized orthogonal frequency division multiplexing (OFDM)-based candidate waveforms are recommended by 3GPP as the waveforms for the NR in order to preserve the advantages of OFDM as well as maintain backward compatibility. 5G New Radio enables diverse service support, efficient synchronization and channel adaptability using a multinumerology concept, which defines a family of parameters of the parent waveform, that are related to each other by scaling. The major design challenges in the LEO satellite scenario are power limited link budget and high Doppler effects which can be addressed by choosing waveforms with small peak to average power ratio (PAPR) and sub-carrier bandwidth adaptation respectively. Hence, the selection of the right waveform and numerology is of prime relevance for the proper adaptation of 5G NR for LEO satellite terrestrial integration. The performance evaluation of the new air interface waveforms, with different numerologies, are carried out under the effect of carrier frequency offset (CFO), multipath effects, non-linearity, phase noise and additive white Gaussian noise (AWGN).

A low complexity massive multiple-input multipleoutput (MIMO) technique is studied with a geometry-based stochastic channel model, called COST 2100 model. We propose to exploit the discrete-time Fourier transform of the antenna correlation function to perform user scheduling. The proposed algorithm relies on a trade off between the number of occupied bins of the eigenvalue spectrum of the channel covariance matrix for each user and spectral overlap among the selected users. We next show that linear precoding design can be performed based only on the channel correlation matrix. The proposed scheme exploits the angular bins of the eigenvalue spectrum of the channel covariance matrix to build up an “approximate eigenchannels” for the users. We investigate the reduction of average system throughput with no channel state information at the transmitter (CSIT). Analysis and numerical results show that while the throughput slightly decreases due to the absence of CSIT, the complexity of the system is reduced significantly.

High mobility scenarios may be typical for different applications such as low earth orbit (LEO) satellite and vehicle-to-everything (V2X) communications. A standardized approach to dealing with high mobility scenarios is using flexible sub-frame structures including a higher pilot density in the time domain, which leads to reduced spectrum efficiency. We propose a supplementary algorithm to improve multiple antenna receiver performance in high mobility scenarios for the given sub-frame structure compared to the conventional 3GPP pilot and data based interference rejection receivers. The main feature of high mobility (non-stationary) scenarios is that different symbols in the desired signal sub-frame may be received under different propagation and/or interference conditions. Recently, we have addressed a non-stationary interference rejection scenario in slowly varying propagation environment with asynchronous (intermittent) interference by means of developing an interference rejection combining algorithm, where the pilot based estimate of the interference plus noise covariance matrix is regularized by the data based estimate of the covariance matrix. In this paper, we: 1) extend the data regularized solution to the general high mobility scenarios, and 2) demonstrate its efficiency compared to the conventional pilot and data based receivers for different sub-frame formats in the uplink transmissions in the LEO satellite scenario with high residual Doppler frequency with and without hardware impairments.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms' efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve system throughput and cell edge performance. However, CoMP implementation requires complex beamforming/scheduling design, increased backhaul bandwidth, additional pilot overhead and precise synchronisa-tion. Cooperation needs to be limited to a few cells only due to this imposed overhead and complexity. Hence, small CoMP clusters will need to be formed in the network. In this paper, we first present a self organising, user-centric CoMP clustering algorithm in a control/data plane separation architecture (CDSA), proposed for 5G to maximise spectral efficiency (SE) for a given maximum cluster size. We further utilise this clustering algorithm and introduce a novel two-stage re-clustering algorithm to reduce high load on cells in hotspot areas and improve user satisfaction. Stage-1 of the algorithm utilises maximum cluster size metric to introduce additional capacity in the system. A novel re-clustering algorithm is introduced in stage-2 to distribute load from highly loaded cells to neighbouring cells with less load for multi-user (MU) joint transmission (JT) CoMP case. We show that unsatisfied users due to high load can be significantly reduced with minimal impact on SE.

A novel blind multi-user detection scheme is developed, which shows clear superiority to the conventional subspace-based blind multi-user detector by improving the condition of the signal subspace. The performance advantages are shown to be more obvious as the number of active users increases, which makes it an attractive solution in heavily loaded systems.

We address the problem of error propagation inherent in the VBLAST detection process. To this end, two improved VBLAST schemes are proposed. The first one replaces hard decision with soft decision; whereas the other also utilizes soft symbol estimate, but in the meantime exploits the noncircular nature of the residual co-antenna interference (CAI) and noise, it involves refining the error criterion and nulling filter. Simulation results show that both schemes outperform their conventional counterpart and utilization of noncircular CAI significantly alleviates the error propagation problem and improves the performance of the VBLAST detection.

In this treatise, we introduce a novel polarization modulation (PM) scheme, where we capitalize on the reconfigurable polarization antenna design for exploring the polarization domain degrees of freedom, thus boosting the system throughput. More specifically, we invoke the inherent properties of a dual polarized (DP) antenna for transmitting additional information carried by the axial ratio (AR) and tilt angle of elliptic polarization, in addition to the information streams transmitted over its vertical (V) and horizontal (H) components. Furthermore, we propose a special algorithm for generating an improved PM constellation tailored especially for wireless PM modulation. We also provide an analytical framework to compute the average bit error rate (ABER) of the PM system. Furthermore, we characterize both the discrete-input continuousoutput memoryless channel (DCMC) capacity and the continuous-input continuous-output memoryless channel (CCMC) capacity as well as the upper and lower bounds of the CCMC capacity. The results show the superiority of our proposed PM system over conventional modulation schemes in terms of both higher throughput and lower BER. In particular, our simulation results indicate that the gain achieved by the proposed Q-dimensional PM scheme spans between 10dB and 20dB compared to the conventional modulation. It is also demonstrated that the PM system attains between 54% and 87.5% improvements in terms of ergodic capacity. Furthermore, we show that this technique can be applied to MIMO systems in a synergistic manner in order to achieve the target data rate target for 5G wireless systems with much less system resources (in terms of bandwidth and thenumber of antennas) compared to existing MIMO techniques.

Multiple-input multiple-output (MIMO) technology has been extensively investigated for achieving spatial multiplexing and diversity gains. However, the introduction of multiple radio-frequency (RF) chains at the MIMO transmitter becomes a bottleneck due to high implementation cost. To address this challenge, we propose a novel phase-shifter-aided spatial multiplexing (PSSM) scheme, which utilizes phase shifters for multi-stream transmissions with reduced RF chains. Furthermore, the multiplexing-diversity tradeoff is studied through theoretical analysis for such system to illustrate its advantage, meanwhile the performance bound in terms of bit-error rate (BER) is quantified. Finally, simulation results demonstrate the superiority of the proposed PSSM scheme over its conventional counterparts in terms of BER, especially under high transmission rate requirements.

This paper investigates the maximal achievable rate for a given average error probability and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength channel coding achievability bound and a converse bound based on the Berry-Esseen theorem, the Mellin transform and the mutual information. Numerical evaluation shows fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength.

Mixed numerology is considered as a promising way to support the extremely diverse service requirements of the fifth generation (5G) wireless communication systems. However, inter‐numerology interference (INI) will be introduced with multiple numerologies coexisting in the same frequency band, which may degrade the system performance significantly. To solve the problem, a promising low out‐of‐band‐emission (OoBE) waveform, i.e. windowed orthogonal frequency division multiplexing (W‐OFDM), is investigated in this chapter. In addition, a theoretical model of INI is established, and the analytical expression of its power is derived as a function of the channel frequency response of the interfering subcarrier, the spectral distance separating the aggressor and the victim subcarrier, and the overlapping windows generated by the interferer's transmitter windows and the victim's receiver window. At last, simulations are performed to verify the effectiveness of the derivations.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent Omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedure to solve it. Simulation results validate the proposed algorithms' efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, a localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatialtemporal- domain doubly random due to the target mobility, environment dynamics and different surroundings at different reference nodes. Firstly, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm’s Law in circuit theory. Secondly, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Thirdly, the convergence conditions, convergence properties and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme. Finally, numerical simulations are presented to corroborate the EP analysis.

This paper presents a novel iterative receiver strategy incorporating widely linear filtering for uplink Orthogonal Frequency Division Multiple Access (OFDMA) multiuser multiple-input, multiple-output (MIMO) systems. The proposed iterative receiver scheme achieves better performance without the loss of spectrum efficiency compared to the conventional iterative receivers; The superiority of the investigated scheduler coupled with the innovative iterative receiver scheme over conventional solutions is verified by both simulation and analytical results. © VDE Verlag GMBH.

—By introducing nonorthogonal multiple access (NOMA) based millimeter wave (mmW) communication, it can significantly improve the transmission efficiency of mobile edge computing (MEC) offloading. In this paper, we are motivated to investigate the resource allocation (RA) problem of the NOMA-mmW scheme based MEC offloading system, by jointly optimizing the beamwidth, user equipment (UE) scheduling and transmit power. To tackle the mixed integer nonlinear programming (MINLP) problem of delay minimization, we develop the alternative optimization (AO) approach based RA scheme, namely AO-RA, to obtain the close-optimum solutions. In the AO-RA scheme, we propose the matrix control many-to-one with externality (MC-M2OE) algorithm, to find the best UE scheduling for the NOMA groupings of different types of UEs. Up on the above, we further design the joint beamwidth and transmit power (JBTP) algorithm, which determines the optimal beamwidth and transmit power for the MEC offloading transmissions. Our simulation results show the effectiveness of the proposed AO-RA scheme in minimizing the offloading delay, where our MC-M2OE and JBTP algorithms can significantly outperform the existing approaches. From the simulation results, we may conclude that, it needs to carefully address the trade-off between beam alignment overhead and transmission gain, while properly balancing the loading among different NOMA groups, for the practical consideration of NOMA-mmW MEC technology.

Universal filtered multi-carrier (UFMC) systems offer a flexibility of filtering arbitrary number of subcarriers to suppress out of band (OoB) emission, while keeping the orthogonality between subcarriers and robustness to transceiver imperfections. Such properties enable it as a promising candidate waveform for Internet of Things (IoT) communications. However, subband filtering may affect system performance and capacity in a number of ways. In this paper, we first propose the conditions for interference-free one-tap equalization and corresponding signal model in the frequency domain for UFMC system. The impact of subband filtering on the system performance is analyzed in terms of average signal-to-noise ratio (SNR), capacity and bit error rate (BER) and compared with the orthogonal frequency division multiplexing (OFDM) system. This is followed by filter length selection strategies to provide guidelines for system design. Next, by taking carrier frequency offset (CFO), timing offset (TO), insufficient guard interval between symbols and filter tail cutting (TC) into consideration, an analytical system model is established. In addition, a set of optimization criteria in terms of filter length and guard interval/filter TC length subject to various constraints is formulated to maximize the system capacity. Numerical results show that the analytical and corresponding optimal approaches match the simulation results, and the proposed equalization algorithms can significantly improve the BER performance.

MIMO. Transmit. Diversity: Theoretical. Analyses. and. Practical. Applications. Pei Xiao1, Zihuai Lin2 and Jian Mao3 Abstract: In this chapter, we study transmit diversity scheme employed in multipleinput, multiple-output (MIMO) systems and ...

This paper highlights the crucial importance of polarization control within 5G wireless communication. We propose a compact polarization converter on a thin ferrite-based metasurface, which enables flexible manipulation of polarization in the reflected waves. The direction of applied magnetics bias allows the metasurface to polarize reflected waves in either co-or cross-polarization with respect to the incident wave. To optimize ferrite utilization, the adoption of a cubic lattice structure in metasurface design is recommended. This design approach has successfully delivered efficient polarization conversion and showcased impressive frequency reconfigurability. Each unit cell within the proposed metasurface can be independently controlled for spatial modulation. Utilizing the distinct material properties associated with various polarizations, the suggested metasurface exhibits remarkable potential in creating reflective intelligent surfaces. These surfaces have the capacity to substantially enhance coverage and elevate the performance of 5G networks. Index Terms—Ferrite, metasurface, polarization control, 5G, non-reciprocal wave propagation, Faraday rotation. I. INTRODUCTION As the demand for high-speed, low-latency, and reliable wireless communication continues to surge, the development of advanced technologies to enhance 5G networks becomes dominant. Metasurfaces in 5G applications enable precise beamforming and enhance wireless communication efficiency, revolutionizing the way data is transmitted and received, thus shaping the future of high-speed connectivity [1]–[3]. In this context, the manipulation of electromagnetic wave polarization has emerged as a crucial factor for improving the performance and efficiency of communication systems. Polarization converters, which can transform incident waves into desired polarizations, have garnered significant attention [4], [5]. This paper considers solutions in the form of a compact polarization converter, engineered on a thin ferrite-based metasurface, that holds great potential for revolutionizing 5G wireless communication. In previous investigations, various geometries have been investigated for achieving linear-to-cross polarization conversion , such as H-shaped metallic structures [6], the double-split ring resonator [7], and double V-shaped resonators [8]. Additionally, the effective realization of linear-to-circular polarization conversion has been demonstrated through designs like the Jerusalem Cross resonator and corner-truncated patch resonator [9]. Notably, a reconfigurable metasurface,

Full-duplex transceivers enable transmission and reception at the same time on the same frequency, and have the potential to double the wireless system spectral efficiency. Recent studies have shown the feasibility of full-duplex transceivers. In this paper, we address the radio resource allocation problem for full-duplex system. Due to the self-interference and inter-user interference, the problem is coupled between uplink and downlink channels, and can be formulated as joint uplink and downlink sum-rate maximization. As the problem is non-convex, an iterative algorithm is proposed based on game theory by modelling the problem as a noncooperative game between the uplink and downlink channels. The algorithm iteratively carries out optimal uplink and downlink resource allocation until a Nash equilibrium is achieved. Simulation results show that the algorithm achieves fast convergence, and can significantly improve the full-duplex performance comparing to the equal resource allocation approach. Furthermore, the full-duplex system with the proposed algorithm can achieve considerable gains in spectral efficiency, that reach up to 40%, comparing to half-duplex system.

In the conventional space-time coding technique, nT radio frequency (RF) chains are employed to transmit signals simultaneously from nT transmit antennas. A low-complexity transmit diversity scheme with nT=2 transmit antennas is proposed, which employs only one RF chain as well as a low-complexity switch for transmission.

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.

Cooperative network localization plays an important role in wireless sensor network (WSN), wherein neighboring sensor nodes will help each other to calibrate their locations. However, due to the dynamic wireless propagation environment and different surroundings, the measurement accuracy at different network nodes is different and varies over time. In this paper, the uncertainties in both measurement accuracy and reference node locations are considered to account for the impact of different surrounding environments and the initial node location errors on the cooperative network localization. A mean-field variational inference-based positioning (VIP) algorithm is proposed for cooperative network localization. The mechanism of the proposed VIP algorithm, the convergence properties, implementation complexity, and the parallel implementation structure are presented to show that the VIP algorithm provides an effective mechanism to incorporate and share the localization information among all network nodes for an improved localization performance. Finally, a concise Cramer-Rao lower bound (CRLB) is derived to reveal the principle of localization error propagation. It is disclosed that the localization error propagation principle is similar to the Ohm’s Law in circuit theory, which provides a new insight into the impact of the measurement accuracy, the reference node location errors and the number of reference nodes on the cooperative network localization performance.

The global telecommunication market aims to fulfil future ubiquitous coverage and rate requirements by integrating terrestrial communications with multiple spot beam high throughput satellites (HTS). In this paper a new scheme is proposed to connect multiple Low Earth Orbit (LEO) satellites in a constellation to a single gateway to support integrated-satellite-terrestrial networks. A single gateway with multiple steerable antenna arrays is proposed for reduction in gateway numbers and cost. Using a power allocation strategy, the target is to maximize the gateway link capacity of the HTS-LEO satellites for operation including feasible used cases studies of 3GPP for necessary adaptations in a 5G system. Firstly, an objective function is established to find the optimal power levels required. Secondly the interference from neighbouring satellite beams is considered to achieve maximum capacity. Mathematical formulations are developed for this non-convex problem. Simulation results show that the proposed system architecture improves capacity and meets the dynamic demand better than traditional methods.

The conventional interference cancellation receiver is subject to performance degradation due to incorrect decisions on interference subtracted from the received signal. This paper aims at deriving algorithms to improve the performance of interference cancellation and channel estimation in an uncoded asynchronous DS-CDMA system with orthogonal modulation. Two soft cancellation schemes, one based on the maximum a posterior (MAP), the other based on the nonlinear minimum mean square error (MMSE) criterion are presented and proved to be superior to the conventional PIC scheme with minor increase in complexity. Furthermore, the best system performance (2dB gain in a 21-user system) is observed when the derived soft information is also used for channel estimation.

The system under study is a convolutionally coded and orthogonally modulated DS-CDMA system in time-varying frequency selective Rayleigh fading channels. After convolutional encoding and block interleaving, information bits are mapped to M- ary orthogonal Walsh sequences. In conventional systems, Mary symbol demodulation and convolutional decoding are conducted separately in the receiver, only hard decisions are passed between these two blocks. In this paper, we propose an integrated scheme based on some soft demodulation and decoding algorithms. Instead of making hard decision on the transmitted M-ary symbols from the received observations, we compute the reliability value for the code bits from which orthogonal symbols are formed. This soft information is then deinterleaved and decoded. The detected bits are fed back to demodulator for channel estimation and multiuser detection. For channel decoding, we use Log-MAP algorithm instead of VA (Viterbi algorithm) for better performance. Maximum achievable performance for the system is obtained by iterating this joint soft demodulation and Log-MAP decoding process. The performance of this strategy is evaluated numerically and proved to significantly outperform the conventional partitioned and hard decision based scheme.

Exploiting channel reciprocity, time-divisionduplexing (TDD) operated massive multiple-input multipleoutput (MIMO) systems are able to acquire the channel state information with a reasonable overhead of channel estimation. However, in practical scenarios, the imperfections in channel reciprocity can significantly degrade the system performance. In this work, we propose a novel self calibration scheme for the maximum ratio transmission in TDD multi-user massive MIMO systems to compensate for the imperfect channel reciprocity, with considerations of imperfect channel estimation. The proposed scheme shows the greater robustness to a compound effect of channel reciprocity error and channel estimation error, compared with the traditional self calibration scheme that is widely used in massive MIMO systems.

The uplink of a reconligurable intelligent surfaces (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel slate information (CSI) is estimated using uplink pilots. First, we derive analytical expressions for the achievable rate of the system with zero forcing (ZF) receiver, taking into account the effects of pilot contamination, channel estimation error and the distributed RISs. The max-min rale optimization problem is considered with per-user power constraints. To solve this non-convex problem, we propose to decouple the original optimization problem into two sub-problems, namely, phase shift design problem and power allocation problem. The power allocation problem is solved using a standard geometric programming (GP) whereas a semidefinite programming (SDP) is utilized to design the phase shifts. Moreover, the Taylor series approximation is used to convert the nonconvex constraints into a convex form. An iterative algorithm is proposed whereby at each iteration, one of the sub-problems is solved while the other design variable is fixed. The max-min user rate of the RIS-aided cell-free massive MIMO system is compared to that of conventional cell-free massive MIMO. Numerical results indicate the superiority of the proposed algorithm compared with a conventional cell-free massive MIMO system. Finally, the convergence of the proposed algorithm is investigated.

To alleviate the downlink training and uplink feedback overhead in frequency-division duplexing massive multiple-input multiple-output systems, a two-stage precoder is proposed for hybrid analog and digital precoding. The analog beamformer and digital precoder are jointly designed by leveraging the signal-to-leakage-plus-noise ratio metric. The design of the analog beamformer is based only on the long-term channel statistics information, exploiting both the channel mean and low rank covariance statistics, whereas the digital precoder utilizes the knowledge of an effective channel with significantly reduced dimensionality. Consequently, we can considerably reduce the signalling and feedback overhead to the dimensions of the resultant effective channel. For comparison purposes, the cases of full channel state information at the transmitter (CSIT) and statistical CSIT are also investigated. It is shown that the digital precoder design problem reduces to the generalized Rayleigh quotient problem, while the analog beamformer design problem reduces to the quotient trace problem. These dimensionality reduction problems are solved via the generalized eigenvalue decomposition method.

This paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-Signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

The advancement of mobile internet technology has created opportunities for integrating the Industrial Internet of Things (IIoT) and edge computing in smart manufacturing. These sustainable technologies enable intelligent devices to achieve high-performance computing with minimal latency. This paper introduces a novel approach to deploy edge computing nodes in smart manufacturing environments at a low cost. However, the intricate interactions among network sensors, equipment, service levels, and network topologies in smart manufacturing systems pose challenges to node deployment. To address this, the proposed sustainable game theory method identifies the optimal edge computing node for deployment to attain the desired outcome. Additionally, the standard design of Software Defined Network (SDN) in conjunction with edge computing serves as forwarding switches to enhance overall computing services. Simulations demonstrate the effectiveness of this approach in reducing network delay and deployment costs associated with computing resources. Given the significance of sustainability, cost efficiency plays a critical role in establishing resilient edge networks. Our numerical and simulation results validate that the proposed scheme surpasses existing techniques like shortest estimated latency first (SELF), shortest estimated buffer first (SEBF), and random deployment (RD) in minimizing the total cost of deploying edge nodes, network delay, packet loss, and energy consumption.

In this paper, we investigate the hybrid precoding design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS). Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.

In this letter, a novel variant of sparse code multiple access (SCMA), called codeword position index based SCMA (CPI-SCMA), is proposed. In this scheme, the information is transmitted not only by the codewords in a M-point SCMA codebook, but also by the indices of the codeword positions in a data block. As such, both the power and transmission efficiency (TE) can be improved. Furthermore, CPI-SCMA can achieve better error rate performance compares to conventional SCMA (C-SCMA) in the region of moderate and high SNRs.

A multicarrier-division duplex (MDD)-based cell-free (CF) scheme, namely MDD-CF, is proposed, which enables downlink (DL) data and uplink (UL) data or pilots to be concurrently transmitted on mutually orthogonal subcarriers in distributed CF massive MIMO (mMIMO) systems. To demonstrate the advantages of MDD-CF, we firstly study the spectral-efficiency (SE) performance in terms of one coherence interval (CT) associated with access point (AP)-selection, power- and subcarrier-allocation. Since the formulated SE optimization is a mixed-integer non-convex problem that is NP-hard to solve, we leverage the inherent association between involved variables to transform it into a continuous-integer convex-concave problem. Then, a quadratic transform (QT)-assisted iterative algorithm is proposed to achieve SE maximization. Next, we extend our study to the case of one radio frame consisting of several CT intervals. In this regard, a novel two-phase CT interval (TPCT) scheme is designed to not only improve the SE in radio frame but also provide consistent data transmissions over fast time-varying channels. Correspondingly, to facilitate the optimization, we propose a two-step iterative algorithm by building the connections between two phases in TPCT through an iteration factor. Simulation results show that, MDD-CF can significantly outperform in-band full duplex (IBFD)-CF due to the efficient interference management. Furthermore, compared with time-division duplex (TDD)-CF, MDD-CF is more robust to high-mobility scenarios and achieves better SE performance.

Generalized index modulation (GIM) which implicitly conveys information by the activated indices is a promising technique for next-generation wireless networks. Due to the prohibitive challenge of bit-to-index combination (IC) mapping optimization, conventional GIM system obtains the bit-to-IC mapping table randomly, which may suffer from some performance loss. To circumvent this issue, we propose a low-complexity graph theory assisted bit-to-IC gray coding for GIM systems by minimizing the average hamming distance (HD) between any two ICs having one different value. Specifically, we decompose and transform the optimization problem into two subproblems using the graph theory, i.e., 1) Select an IC set whose corresponding graph has the minimum degree; 2) Design a bit-to-IC mapping principle to minimize the weight of the selected graph. Low complexity algorithms are developed to solve the subproblems with a significant reduced complexity. Both simulation and theoretical results are shown that the GIM systems with our proposed mapping table are capable of providing significant performance gains over the conventional counterparts without the need for any additional feedback-link and without extra computational complexity. It is also shown that the proposed bit-to-IC mapping table is straightforward for any GIM systems over generalized fading channels.

Spectrum sharing and employing highly directional antennas in the mm-wave bands are considered among the key enablers for 5G networks. Conventional interference avoidance techniques like listen-before-talk (LBT) may not be efficient for such coexisting networks. In this paper, we address a coexistence mechanism by means of distributed beam scheduling with minimum cooperation between spectrum sharing subsystems without any direct data exchange between them. We extend a “Good Neighbor” (GN) principle initially developed for decentralized spectrum allocation to the distributed beam scheduling problem. To do that, we introduce relative performance targets, develop a GN beam scheduling algorithm, and demonstrate its efficiency in terms of performance/complexity trade off compared to that of the conventional selfish (SLF) and recently proposed distributed learning scheduling (DLS) solutions by means of simulations in highly directional antenna mm-wave scenarios.

A novel iterative detection scheme for MIMO-OFDM systems is proposed in this work. We show that the existing detection schemes are sub-optimum and the iterative process can be optimized by utilizing the non-circular property of the residual interference after interference cancellation. Results show that the proposed iterative scheme outperforms the conventional iterative soft interference cancellation (ISIC) and V-BLAST schemes by about 1.7 and 4.0 dB, respectively, in a 4 × 4 antennas system over exponentially distributed eleven path channels.

Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.

This paper presents a novel method to estimate the frequency offset between a mobile phone and the infrastructure when the mobile phone initially attaches to the LTE network. The proposed scheme is based on PRACH (Physical Random Access Channel) preambles and can significantly reduce the complexity of preamble detection at the eNodeB side.

By performing the Floquet-mode analysis of a periodic slotted waveguide, a multiple-beam leaky wave antenna is proposed in the millimetre-wave (mmW) band. Considering the direction of surface current lines on the broad/side-walls of the waveguide, the polarization of constructed beams are also controlled. The simulation results are well matched with the initial mathematical analysis.

This paper proposes a low-complexity hybrid beamforming design for multi-antenna communication systems. The hybrid beamformer comprises of a baseband digital beamformer and a constant modulus analog beamformer in radio frequency (RF) part of the system. As in Singular-Value-Decomposition (SVD) based beamforming, hybrid beamforming design aims to generate parallel data streams in multi-antenna systems, however, due to the constant modulus constraint of the analog beamformer, the problem cannot be solved, similarly. To address this problem, mathematical expressions of the parallel data streams are derived in this paper and desired and interfering signals are specified per stream. The analog beamformers are designed by maximizing the power of desired signal while minimizing the sum-power of interfering signals. Finally, digital beamformers are derived through defining the equivalent channel observed by the transmitter/receiver. Regardless of the number of the antennas or type of channel, the proposed approach can be applied to wide range of MIMO systems with hybrid structure wherein the number of the antennas is more than the number of the RF chains. In particular, the proposed algorithm is verified for sparse channels that emulate mm-wave transmission as well as rich scattering environments. In order to validate the optimality, the results are compared with those of the state-of-the-art and it is demonstrated that the performance of the proposed method outperforms state-of-the-art techniques, regardless of type of the channel and/or system configuration.

In this paper, we address the problem of interference mitigation with data pre-processing in the 4G uplink systems, and propose to use the Grubbs/Wright algorithm to detect and remove the interference contaminated data. The Markov algorithm is also applied to correct the system errors. The pre-processed data are used for channel estimation and data detection in base stations

The Integrated Laser Communication/Ranging System, which uses coded signal as the ranging information carrier, is of great importance to the next large-capacity inter-satellite information network. In this paper, a system design with high-sensitivity feedback-homodyne detection scheme and asynchronous ranging algorithm is demonstrated with real-time FPGA implementation. The parallel FFT estimation is applied to improve the speed and range of wavelength drift tracking, which can handle dynamic wavelength drift up to 2.4 pm/s (300 MHz/s). Meanwhile, for clock sources with subtle dynamic frequency offset and sufficient stability, the proposed fractional symbol ranging method is proven to achieve millimeter-level measurement accuracy. The designed system is shown to perform well in terms of both laser linewidth tolerance and noise resistance.

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

Nowadays, system architecture of the fifth generation (5G) cellular system is becoming of increasing interest. To reach the ambitious 5G targets, a dense base station (BS) deployment paradigm is being considered. In this case, the conventional always-on service approach may not be suitable due to the linear energy/density relationship when the BSs are always kept on. This suggests a dynamic on/off BS operation to reduce the energy consumption. However, this approach may create coverage holes and the BS activation delay in terms of hardware transition latency and software reloading could result in service disruption. To tackle these issues, we propose a predictive BS activation scheme under the control/data separation architecture (CDSA). The proposed scheme exploits user context information, network parameters, BS sleep depth and measurement databases to send timely predictive activation requests in advance before the connection is switched to the sleeping BS. An analytical model is developed and closed-form expressions are provided for the predictive activation criteria. Analytical and simulation results show that the proposed scheme achieves a high BS activation accuracy with low errors w.r.t. the optimum activation time.

Low density signature orthogonal frequency division multiplexing (LDS-OFDM) and low density parity-check (LDPC) codes are multiple access and forward error correction (FEC) techniques, respectively. Both of them can be expressed by a bipartite graph. In this paper, we construct a joint sparse graph combining the single graphs of LDS-OFDM and LDPC codes, namely joint sparse graph for OFDM (JSG-OFDM). Based on the graph model, a low complexity approach for joint multiuser detection and FEC decoding (JMUDD) is presented. The iterative structure of JSG-OFDM receiver is illustrated, and its extrinsic information transfer (EXIT) chart is researched. Furthermore, design guidelines for the joint sparse graph are derived through the EXIT chart analysis. By offline optimization of the joint sparse graph, numerical results show that the JSG-OFDM brings about 1.5 – 1.8 dB performance improvement at bit error rate (BER) of 10 5 over similar well-known systems such as grouporthogonal multi-carrier code division multiple access (GO-MCCDMA), LDS-OFDM and turbo structured LDS-OFDM.

In this paper, we investigate resource allocation in the multicarrier spread spectrum systems, especially in the multicell downlink multicarrier direct-sequence code division multiple-access (MC DS-CDMA) systems. The allocation of resources including subcarriers and spreading codes aims to maximize the system reliability, thereby resulting in the high-reliability mutlicarrier systems. For the sake of achieving low-complexity, we develop the novel resource allocation framework. We propose two resource allocation algorithms, which are the simplified heuristic subcarrier- and code-allocation (SHSC) algorithm and the enhanced heuristic subcarrier- and code-allocation (EHSC) algorithm. The two proposed algorithms can find the promising sub-optimum solutions to the mixed integer nonconvex resource allocation problem. The SHSC algorithm has lower complexity and demands less backhaul resources than the EHSC algorithm. In return, the EHSC algorithm performs better than the SHSC algorithm. Nevertheless, we show that both algorithms significantly outperform the existing algorithms, while approaching the optimal algorithm of high complexity

This paper investigates a wireless powered sensor network (WPSN), where multiple sensor nodes are deployed to monitor a certain external environment. A multi-antenna power station (PS) provides the power to these sensor nodes during wireless energy transfer (WET) phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center (FC) during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, the paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading based Stackelberg game, linear energy trading based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.

—This paper considers a two-user downlink multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system using minimum mean-squared error (MMSE) detection under imperfect channel estimation. By taking account of both errors in channel estimation and MMSE detection, we derive approximated users' capacities and design a closed-form power allocation scheme to maximize the minimum (max-min) of them. The design problem is equivalent to max-min optimization of users' signal-to-interference-plus-noise ratios (SINRs), and the solution can be obtained by SINR balancing. The proposed power allocation scheme involves solving two quadratic equations, and is easy to implement in practical applications. As compared with an existing robust MIMO-NOMA power allocation method based on generalized singular value decomposition and SINR balancing, the proposed one offers slightly worse bit-error-rate performance with much lower complexity.

—In this paper, a novel supplementary index bit aided transmit diversity (SIB-TD) approach is proposed for an enhanced discrete cosine transform based orthogonal frequency division multiplexing with index modulation (EDCT-OFDM-IM) system. Specifically, conventional index modulation (IM) is employed for the first antenna, i.e., the main branch, and the non-activated subcarriers' indices in one index modulation group are utilized for the IM mapping on the same index modulation group for the second antenna, i.e., the diversity branch. Hence, each subcarrier can not be synchronously activated on the two antennas. Finally, the non-activated subcarriers of one antenna will transmit the same modulated symbols of the other antenna to exploit the diversity gain. At the receiver, a maximum likelihood group-wise receiver is also developed by detecting the main and diversity branches jointly. Simulation results demonstrate the superiority of the proposed scheme over both the conventional EDCT-OFDM-IM and the DFT-OFDM with Alamouti code either with or without IM respectively, even under the imperfect channel estimation.

Grant-free non-orthogonal multiple access (NOMA) scheme is considered as a promising candidate for the enabling of massive connectivity and reduced signalling overhead for Internet of Things (IoT) applications in massive machine-type communication (mMTC) networks. Exploiting the inherent nature of sporadic transmissions in the grant-free NOMA systems, compressed sensing based multiuser detection (CS-MUD) has been deemed as a powerful solution to user activity detection (UAD) and data detection (DD). In this paper, block coordinate descend (BCD) method is employed in CS-MUD to reduce the computational complexity. We propose two modified BCD based algorithms, called enhanced BCD (EBCD) and complexity reduction enhanced BCD (CR-EBCD), respectively. To be specific, by incorporating a novel candidate set pruning mechanism into the original BCD framework, our proposed EBCD algorithm achieves remarkable CS-MUD performance improvement. In addition, the proposed CR-EBCD algorithm further ameliorates the proposed EBCD by eliminating the redundant matrix multiplications during the iteration process. As a consequence, compared with the proposed EBCD algorithm, our proposed CR-EBCD algorithm enjoys two orders of magnitude complexity saving without any CS-MUD performance degradation, rendering it a viable solution for future mMTC scenarios. Extensive simulation results demonstrate the bound-approaching performance as well as ultra-low computational complexity.

Sparse Code Multiple Access (SCMA) is a disruptive code-domain non-orthogonal multiple access (NOMA) scheme to enable \color{black}future massive machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive sparse codebooks (CBs). Efficient multiuser detection is carried out at the receiver by employing the message passing algorithm (MPA) that exploits the sparsity of CBs to achieve error performance approaching to that of the maximum likelihood receiver. In spite of numerous research efforts in recent years, a comprehensive one-stop tutorial of SCMA covering the background, the basic principles, and new advances, is still missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we provide a holistic introduction to the principles of SCMA encoding, CB design, and MPA based decoding in a self-contained manner. As an ambitious paper aiming to push the limits of SCMA, we present a survey of advanced decoding techniques with brief algorithmic descriptions as well as several promising directions.

In this work, we provide the first attempt to evaluate error performance of Rate-Splitting (RS) based transmission strategies with constellation-constrained coding/modulation. The consider scenario is an overloaded multigroup multicast, where RS can mitigate the inter-group interference thus achieve a better max-min fair group rate over conventional transmission strategies. We bridge the RS-based rate optimization with modulationcoding scheme selection, and implement them in a developed transceiver framework with either linear or non-linear receiver, where the latter equips with a generalized sphere decoder. Simulation results of a coded bit error rate demonstrate that, while the conventional strategies suffer from the error floor in the considered scenario, the RS-based strategy delivers a superior performance even with low complexity receiver techniques. The proposed analysis, transceiver framework and evaluation methodology provide a generic baseline solution to validate the effectiveness of the RS-based system design in practice. Index Terms—Rate-splitting, overloaded system, multigroup multicast, rank-deficient, generalized sphere decoder, coded bit error rate.

Sparse code multiple access (SCMA) is a promising air interface candidate technique for next generation mobile networks. By introducing the Tent map in the Chaos theory, we propose a novel physical layer transmission scheme with codeword level interleaving at the transmitter in this letter, which is termed as interleaver based SCMA (I-SCMA). Simulation results and analysis show that I-SCMA can provide high security performance without any loss in performance and transmission rate, thus constitutes a viable solution for the next generation wireless networks to provide secure communications.

In this paper, the capacity of OFDM/OQAM with isotropic orthogonal transfer algorithm (IOTA) pulse shaping is evaluated through information theoretic analysis. In the conventional OFDM systems the insertion of a cyclic prefix (CP) decreases the system’s spectral efficiency. As an alternative to OFDM, filter bank based multicarrier systems adopt proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. We evaluate the spectral efficiency of OFDM/OQAM systems with IOTA pulse shaping in comparison with conventional OFDM/QAM systems, and our analytical model is further extended in order to gain insights into the effect of utilizing the intrinsic interference on the performance of our system. Furthermore, the spectral efficiency of OFDM/OQAM systems is analyzed when the effect of inter-symbol and inter-carrier interference is considered.

This paper considers multiuser MIMO CDMA systems with high rate space-time linear dispersion codes (LDC) and orthogonal space-time block codes (O-STBC) in time-varying Rayleigh fading MIMO channels. We propose a multi-function process integrating multi-user detection, space-time decoding and symbol demodulation, which can be coupled with soft channel decoding to improve the system performance in an iterative fashion. We show that the space-time coded CDMA systems approach the single-user bound with only two iterations, and full diversity LDCs enable the systems to utilize the time diversity inherent in fast fading channels. The space-time coded CDMA systems are also compared to the MIMO CDMA system based on spatial multiplexing, some recommendations are made on how to design a practical MIMO CDMA system based on the comparative studies.

Sparse Code Multiple Access (SCMA) is a novel non-orthogonal multiple access scheme for 5G systems, in which the logarithm domain message passing algorithm (Log-MPA) is applied at the receiver to achieve near-optimum performance. However, the computational complexity of Log-MPA detector is still a big challenge for practical implementation, especially for energysensitive user equipments in the downlink scenario. In this paper, a Region-Restricted detector with an improved Log-MPA (RRL detector) is proposed for downlink SCMA systems, in which the complexity is reduced from two perspectives. To avoid unnecessary calculations when searching the superposition constellation exhaustively, the proposed RRL detector updates the function nodes only within a restricted search region. While constellation points outside the search region are neglected, the performance is well maintained which is verified by simulations. Besides, the original Log-MPA heavily relies on exponential operations, resulting in high computational complexity. To solve this problem, an improved Log-MPA is also put forward in this paper to make a better compromise between complexity and performance. Simulation results show that the complexity of the RRL detector is reduced considerably while the bit error rate (BER) performance degrades unnoticeably.

Sparse code multiple access (SCMA) is a promising candidate air interface of next-generation mobile networks. In this paper, we focus on a downlink SCMA system where a transmitter sends confidential messages to multiple users in the presence of external eavesdroppers. Consequently, we develop a novel secure transmission approach over physical layer based on a highly structured SCMA codebook design. In our proposed scheme, we rotate the base constellations (BCs) with random angles by extracting channel phases from the channel state information (CSI). By employing randomized constellation rotation (RCR), the security of downlink SCMA can be ensured. In addition, a tight SCMA upper bound is introduced to guide the design of the encrypted codebook. As a result, we propose an approach to avoid the significant error rate performance loss caused by using codebooks that are designed using our method. The proposed upper-bound-aided codebook design scheme can select relatively good codebooks with low complexity. By combining SCMA codebook design and secure communication, our scheme ensures security for massive quantities of users with low encrypted and decrypted complexity at the cost of transmission rate and possible error rate performance loss. Moreover, the proposed scheme can achieve robustness against channel estimation errors. Analyses and Monte Carlo simulations confirm the effectiveness of our scheme.

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity. Index Terms SCMA, codebook design, deep neural network, autoencoder, multi-task learning.

—This article discusses the self-sustainability of recon-figurable intelligent surface (RIS) in wireless powered Internet of Things (IoT) networks. Our vision is that RIS helps improve energy harvesting and data transmission capabilities simultaneously , without the extra utilization of radio frequency (RF) spectrum and energy consumption. The inherent properties of RIS are first discussed to unveil its distinctive features, followed by a broader range of use cases motivated by the RIS as their enabling technology. The focus is on the application of RIS in the wireless powered IoT networks, and its potential to interconnect and support these practical use cases. Such an application is then thoroughly evaluated in a case study of a RIS-assisted wireless powered sensor network (WPSN), with system throughput, energy transmission time consumption, and energy harvesting as the key performance metrics. The comprehensive performance evaluation showcases the self-sustainable property of the RIS being unlocked in the considered scenario, identifying a clear pathway towards the future wireless powered IoT networks. We further pave that pathway by exploring research challenges and open issues related to emerging technological development.

Sparse code multiple access (SCMA) is an emerging paradigm for efficient enabling of massive connectivity in future machine-type communications (MTC). In this letter, we conceive the uplink transmissions of the low-density parity check (LDPC) coded SCMA system. Traditional receiver design of LDPC-SCMA system, which is based on message passing algorithm (MPA) for multiuser detection followed by individual LDPC decoding, may suffer from the drawback of the high complexity and large decoding latency, especially when the system has large codebook size and/or high overloading factor. To address this problem, we introduce a novel receiver design by applying the expectation propagation algorithm (EPA) to the joint detection and decoding (JDD) involving an aggregated factor graph of LDPC code and sparse codebooks. Our numerical results demonstrate the superiority of the proposed EPA based JDD receiver over the conventional Turbo receiver in terms of both significantly lower complexity and faster convergence rate without noticeable error rate performance degradation.

—Unmanned aerial vehicles (UAVs) are useful devices due to their great manoeuvrability for long-range outdoor target tracking. However, these tracking tasks can lead to sub-optimal performance due to high computation requirements and power constraints. To cope with these challenges, we design a UAV-based target tracking algorithm where computationally intensive tasks are offloaded to Edge Computing (EC) servers. We perform joint optimization by considering the trade-off between transmission energy consumption and execution time to determine optimal edge nodes for task processing and reliable tracking. The simulation results demonstrate the superiority of the proposed UAV-based target tracking on the predefined trajectory over several existing techniques. Index Terms—Edge computing (EC), task offloading, un-manned aerial vehicle (UAV)

In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practice available malware features for IoT devices and lack real-time prediction behaviors. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this paper we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic that may not be labeled. In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS. The Self-MDS method considers the IoT input data and the adversarial sample generation in real-time. The GSelf-MDS builds a generative adversarial network model to generate adversarial samples in the self-supervised structure. Finally, ASelf-MDS utilizes three well-known perturbation sample techniques to develop adversarial malware and inject it over the self-supervised architecture. Also, we apply a defence method to mitigate these attacks, namely adversarial self-supervised training to protect the malware detection architecture against injecting the malicious samples. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the Self-MDS method has the most damaging consequences from the attacker's point of view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the ASelf-MDS method is the most devastating algorithm that can plunge the accuracy rate from 98% to 77%.

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

—This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically , the ISAC system combines communication and radar operations and performs on the same hardware platform, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via the reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system in the mmWave band. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the radar desired waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is applied to determine the communication beamforming vector and the IRS phase shift. Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system. Index Terms—Integrated sensing and communications, intelligent reflecting surface, waveform design.

Regarded as one of the most promising transmission techniques for future wireless communications, the discrete cosine transform (DCT) based multicarrier modulation (MCM) system employs cosine basis as orthogonal functions for real-modulated symbols multiplexing, by which the minimum orthogonal frequency spacing can be reduced by half compared to discrete Fourier transform (DFT) based one. With a time-reversed prefilter employed at the front of the receiver, interference-free one-tap equalization is achievable for the DCT-based systems. However, due to the correlated pre-filtering operation in time domain, the signal-to-noise ratio (SNR) is enhanced as a result at the output. This leads to reformulated detection criterion to compensate for such filtering effect, rendering minimum-meansquare- error (MMSE) and maximum likelihood (ML) detections applicable to the DCT-based multicarrier system. In this paper, following on the pre-filtering based DCT-MCM model that build in the literature work, we extend the overall system by considering both transceiver perfections and imperfections, where frequency offset, time offset and insufficient guard sequence are included. In the presence of those imperfection errors, the DCTMCM systems are analysed in terms of desired signal power, inter-carrier interference (ICI) and inter-symbol interference (ISI). Thereafter, new detection algorithms based on zero forcing (ZF) iterative results are proposed to mitigate the imperfection effect. Numerical results show that the theoretical analysis match the simulation results, and the proposed iterative detection algorithms are able to improve the overall system performance significantly.

A cell-free massive multiple-input multiple-output (MIMO) uplink is investigated in this paper. We address a power allocation design problem that considers two conflicting metrics, namely the sum rate and fairness. Different weights are allocated to the sum rate and fairness of the system, based on the requirements of the mobile operator. The knowledge of the channel statistics is exploited to optimize power allocation. We propose to employ large scale-fading (LSF) coefficients as the input of a twin delayed deep deterministic policy gradient (TD3). This enables us to solve the non-convex sum rate fairness trade-off optimization problem efficiently. Then, we exploit a use-and-then-forget (UatF) technique, which provides a closed-form expression for the achievable rate. The sum rate fairness trade-off optimization problem is subsequently solved through a sequential convex approximation (SCA) technique. Numerical results demonstrate that the proposed algorithms outperform conventional power control algorithms in terms of both the sum rate and minimum user rate. Furthermore, the TD3-based approach can increase the median of sum rate by 16%-46% and the median of minimum user rate by 11%-60% compared to the proposed SCA-based technique. Finally, we investigate the complexity and convergence of the proposed scheme. cc Index terms— Cell-free massive MIMO, deep reinforcement learning, fairness, power control, sequential convex approximation .

Robust adaptive multiuser detection schemes are developed for direct-sequence code-division multiple-access (DS-CDMA) multipath frequency-selective fading channels. Multiple access interference (MAI) and intersymbol interference (ISI) are presented in identical format in the expanded signal subspace, which provides convenience for symbol-by-symbol multiuser detection. The proposed multiuse detectors are designed in the expanded signal subspace, and subspace estimation and Kalman filtering algorithms are developed for their adaptive implementation. It is demonstrated by simulation that these adaptive detectors are robust against subspace estimation error and can effectively suppress both MAI and ISI and converge to the optimum SINR.

—Grant-free non-orthogonal multiple access (GF-NOMA) technique is considered as a promising solution to address the bottleneck of ubiquitous connectivity in massive machine type communication (mMTC) scenarios. One of the challenging problems in uplink GF-NOMA systems is how to efficiently perform user activity detection and data detection. In this paper, a novel complexity-reduction weighted block coordinate descend (CR-WBCD) algorithm is proposed to address this problem. To be specific, we formulate the multiuser detection (MUD) problem in uplink GF-NOMA systems as a weighted l2 minimization problem. Based on the block coordinate descend (BCD) framework, a closed-form solution involving dynamic user-specific weights is derived to adaptively identify the active users with high accuracy. Furthermore, a complexity reduction mechanism is developed for substantial computational cost saving. Simulation results demonstrate that the proposed algorithm enjoys bound-approaching detection performance with more than three-order of magnitude computational complexity reduction. Index Terms—Grant-free non-orthogonal multiple access (GF-NOMA), block coordinate descend (BCD), compressed sensing (CS), multiuser detection (MUD).

The use of multiple antennas in combination with advanced detection techniques, such as turbo equalization is an effective means for a Fixed Wireless Access (FWA) system to provide high quality and high data rate services. Alamouti's space-time block code (STBC) with two transmit antennas and one or two receive antennas over frquency selective FWA SUI-3 channels is considered in this paper. We propose a turbo equalization algorithm that aims at exploiting the multipath diversity and reducing the effect of intersymbol iterference (ISI), and in the meantime, keeping the desired feature of the original Alamouti detection algorithm, i.e, achieving spatial diversity with simple linear processing.

This paper introduces a robust variational bayes (Robust-VB) receiver algorithm for joint signal detection, noise covariance matrix estimation and channel impulse response (CIR) tracking in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time varying channels. The variational bayes (VB) framework and turbo principle are combined to accomplish the parameter estimation and data detection. In the proposed Robust-VB receiver, a modified linear minimum mean-square-error interference cancellation (LMMSE-IC) soft detector is developed based on the VB theory, which adaptively sets the log-likelihood ratio (LLR) clipping value according to the reliability of detection on each subcarrier to mitigate the error propagation. Following the signal detection, an adaptive noise covariance matrix estimator is derived for the effective noise covariance estimation. Furthermore, in order to track time varying channels, a VB soft-input Kalman filter (VB-Soft-KF) is first derived. However, unreliable soft symbols introduce outliers, which degrade the performance of VB-Soft-KF. To tackle this problem, we propose a robust VB soft-input Kalman filter (VB-Robust-KF) based on the Huber M estimation theory. Finally, the performance of the proposed algorithm is assessed via simulations, showing the superior performance of the Robust-VB receiver compared to the other benchmark receiver algorithms.

In 4G systems such as Wimax and LTE, system performance suffers from interferences and lack of low complexity algorithms for interference cancellation as well as utilization of optimal resource. In this paper, we propose a PCINR and EM based optimal resource allocation scheme to improve the performance of 4G systems and compensate the performance loss due to spatial correlation and interferences. Furthermore, the SIC technique is employed to mitigate interferences. The performance of the proposed system is evaluated by computer simulations and is shown to outperform the conventional schemes.

Due to the use of an appropriately designed pulse shaping prototype filter, filter bank multicarrier (FBMC) system can achieve low out of band (OoB) emissions and is also robust to the channel and synchronization errors. However, it comes at a cost of long filter tails which may reduce the spectral efficiency significantly when the block size is small. Filter output truncation (FOT) can reduce the overhead by discarding the filter tails but may also significantly destroy the orthogonality of FBMC system, by introducing inter carrier interference (ICI) and inter symbol interference (ISI) terms in the received signal. As a result, the signal to interference ratio (SIR) is degraded. In addition, the presence of intrinsic interference terms in FBMC also proves to be an obstacle in combining multiple input multiple output (MIMO) with FBMC. In this paper, we present a theoretical analysis on the effect of FOT in an MIMO-FBMC system. First, we derive the matrix model of MIMO-FBMC system which is subsequently used to analyze the impact of finite filter length and FOT on the system performance. The analysis reveals that FOT can avoid the overhead in time domain but also introduces extra interference in the received symbols. To combat the interference terms, we then propose a compensation algorithm that considers odd and even overlapping factors as two separate cases, where the signals are interfered by the truncation in different ways. The general form of the compensation algorithm can compensate all the symbols in a MIMO-FBMC block and can improve the SIR values of each symbol for better detection at the receiver. It is also shown that the proposed algorithm requires no overhead and can still achieve a comparable BER performance to the case with no filter truncation.

The aim of this paper is to present the application of the time-reversal space-time coding (TR-STBC) on the broadband fixed wireless access (FWA) systems. In addition to the transmit diversity obtained from the TR-STBC scheme, we also consider the concatenation of TR-STBC and an outer channel code in order to provide coding gain for the FWA systems. A turbo equalization scheme is proposed for the concatenated systems. Different receiver strategies are compared, and their performance/complexity tradeoff is discussed.

Free-space optics (FSO) communication enjoys desirable modulation rates at unexploited frequency bands, however, its application is hindered by atmospheric turbulence which causes phase shifting in laser links. Although a single deformable mirror (DM) adaptive optics (AO) system is a good solution, its performance remains unsatisfactory as the proportion of tilts aberrations becomes relatively high. This condition happens when the incident angle of the laser beam for the optical receiver dynamically shifts. To tackle this problem, we introduce a fast steering mirror (FSM), DM cascaded AO architecture, based upon which we also propose an atmospheric turbulence compensation algorithm. In this paper, we compare the compensation ability of FSM and DM towards tilts aberrations. Furthermore, we gain model matrices for FSM and DM from testbed and simulatively verify the effectiveness of our work. For a Kolmogorov theory-based atmospheric turbulence disturbed incident laser beam where the tilt components take up 80% of the total proportion of wavefront aberrations, our proposed architecture compensates the input wavefront to a residual wavefront root mean square (RMS) of 116 wavelength, compared to 16 wavelength for single DM architecture. The study intends to overcome atmospheric turbulence and has the potential to guide the development of future FSO communications.

We consider a cell-free massive multiple-input multiple-output (MIMO) system where the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU). Zero-forcing technique is used at the CPU to detect the signals transmitted from all users.. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is proposed to convert the problem into a geometric programme (GP). Exploiting a deep convolutional neural network (DCNN) allows us to determine both a mapping from the large-scale fading (LSF) coefficients and the optimal power by solving the optimization problem using the quantized channel. Depending on how the optimization problem is solved, different power control schemes are investigated; i) small-scale fading (SSF)-based power control; ii) LSF use-and-then-forget (UatF)-based power control; and iii) LSF deep learning (DL)-based power control. The SSF-based power control scheme needs to be solved for each coherence interval of the SSF, which is practically impossible in real time systems. Numerical results reveal that the proposed LSF-DL-based scheme significantly increases the performance compared to the practical and well-known LSF-UatF-based power control, thanks to the mapping obtained using DCNN.

This is expected to become more commonplace as semiconductor fabrication moves from thecurrent generation of 65 nm processes to the next 45 nm generations.

This paper exploits a generic downlink symbiotic radio (SR) system, where a Base Station (BS) establishes a direct (primary) link with a receiver having an integrated backscatter device (BD). In order to accurately measure the backscatter link, the backscattered signal packets are designed to have finite block length. As such, the backscatter link in this SR system employs the finite block-length channel codes. According to different types of the backscatter symbol period and transmission rate, we investigate the non-cooperative and cooperative SR (i.e., NSR and CSR) systems, and derive their average achievable rate of the direct and backscatter links, respectively. We formulate two optimization problems, i.e., transmit power minimization and energy efficiency maximization. Due to the non-convex property of these formulated optimization problems, the semidefinite programming (SDP) relaxation and the successive convex approximation (SCA) are considered to design the transmit beamforming vector. Moreover, a low-complexity transmit beamforming structure is constructed to reduce the computational complexity of the SDP relaxed solution. Finally, the simulation results are demonstrated to validate the proposed schemes.

This paper investigates self-backhauling with dual antenna selection at multiple small cell base stations. Both half and full duplex transmissions at the small cell base station are considered. Depending on instantaneous channel conditions, the full duplex transmission can have higher throughput than the half duplex transmission, but it is not always the case. Closed-form expressions of the average throughput are obtained, and validated by simulation results. In all cases, the dual receive and transmit antenna selection significantly improves backhaul and data transmission, making it an attractive solution in practical systems.

The average channel capacity for 3GPP LTE downlink multiuser Multiple Input Multiple Output (MIMO) systems is analyzed in this paper. A packet scheduler is used to exploit the available multiuser diversity in all the three physical domains (i.e., space, time and frequency). A mathematical model is established to derive the channel capacity of multiuser MIMO systems with the frequency domain packet scheduler (FDPS). This work provides a theoretical reference for the future version of the LTE standard and a useful source of information for the practical implementation of the LTE systems.

A novel hybrid multiuser detection scheme that jointly uses linear and nonlinear interference suppression techniques is developed for high-speed direct-sequence code-division multiple-access communications in multipath frequency-selective fading channels. The detector detects signals in a symbol-by-symbol style. Conventional decorrelating detectors suffer from the noise enhancement problem, which becomes more serious for dispersive multipath channels. The proposed detector uses interference cancellation technology to reduce the rank of the expanded signal subspace and hence it preserves the advantages of the expanded decorrelating detector in terms of complete multiple access interference and intersymbol interference suppression and meanwhile avoids its disadvantage in terms of noise enhancement. Computer simulation shows clear superiority of the new detector to other existing methods.

This paper presents an enhanced design of multi-dimensional (MD) constellations which play a pivotal role in many communication systems such as code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are attractive as their structural properties, if properly designed, lead to signal space diversity and hence improved error rate performance. Unlike the existing works which mostly focus on MD constellations with large minimum Euclidean distance (MED), we look for new MD constellations with additional feature that the minimum product distance (MPD) is also large. To this end, a non-convex optimization problem is formulated and then solved by the convex-concave procedure (CCCP). Compared with the state-of-the-art literature, our proposed MD constellations 1 lead to significant error performance enhancement over Rayleigh fading channels whilst maintaining almost the same performance over the Gaussian channels. To demonstrate their application, we also show that these MD constellations give rise to good codebooks in sparse code multiple access systems. Index Terms Multi-dimensional (MD) constellation, code-domain non-orthogonal multiple access (CD-NOMA), sparse code multiple access (SCMA), convex-concave procedure (CCCP), minimum Euclidean distance, minimum product distance.

—In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically , users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multiuser scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system. Index Terms—Mobile edge computing (MEC), Non-orthogonal multiple access (NOMA), physical layer security (PLS), reinforcement learning, sub-channel assignment.

A statistical model is derived for the equivalent signal-to-noise ratio of the Source-to-Relay-to-Destination (S-R-D) link for Amplify-and-Forward (AF) relaying systems that are subject to block Rayleigh-fading. The probability density function and the cumulated density function of the S-R-D link SNR involve modified Bessel functions of the second kind. Using fractional-calculus mathematics, a novel approach is introduced to rewrite those Bessel functions (and the statistical model of the S-R-D link SNR) in series form using simple elementary functions. Moreover, a statistical characterization of the total receive-SNR at the destination, corresponding to the S-R-D and the S-D link SNR, is provided for a more general relaying scenario in which the destination receives signals from both the relay and the source and processes them using maximum ratio combining (MRC). Using the novel statistical model for the total receive SNR at the destination, accurate and simple analytical expressions for the outage probability, the bit error probability, and the ergodic capacity are obtained. The analytical results presented in this paper provide a theoretical framework to analyze the performance of the AF cooperative systems with an MRC receiver.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted millimeter-wave multiple-input single-output downlink wireless communication system. By jointly calculating the active beamforming at the base station and the passive beamforming at the IRS, we aim to minimize the transmit power under the constraint of each user' signal-to-interference-plus-noise ratio. To solve this problem, we propose a low-complexity machine learning-based cross-entropy (CE) algorithm to alternately optimize the active beamforming and the passive beamforming. Specifically, in the alternative iteration process, the zero-forcing (ZF) method and CE algorithm are applied to acquire the active beamforming and the passive beamforming, respectively. The CE algorithm starts with random sampling, by the idea of distribution focusing, namely shifting the distribution towards a desired one by minimizing CE, and a near optimal reflection coefficients with adequately high probability can be obtained. In addition, we extend the original one-bit phase shift at the IRS to the common case with high-resolution phase shift to enhance the effectiveness of the algorithms. Simulation results verify that the proposed algorithm can obtain a near optimal solution with lower computational complexity.

This paper investigates a full duplex wirelesspowered two way communication networks, where two hybrid access points (HAP) and a number of amplify and forward (AF) relays both operate in full duplex scenario. We use time switching (TS) and static power splitting (SPS) schemes with two way full duplex wireless-powered networks as a benchmark. Then the new time division duplexing static power splitting (TDD SPS) and full duplex static power splitting (FDSPS) schemes as well as a simple relay selection strategy are proposed to improve the system performance. For TS, SPS and FDSPS, the best relay harvests energy using the received RF signal from HAPs and uses harvested energy to transmit signal to each HAP at the same frequency and time, therefore only partial self-interference (SI) cancellation needs to be considered in the FDSPS case. For the proposed TDD SPS, the best relay harvests the energy from the HAP and its self-interference. Then we derive closed-form expressions for the throughput and outage probability for delay limited transmissions over Rayleigh fading channels. Simulation results are presented to evaluate the effectiveness of the proposed scheme with different system key parameters, such as time allocation, power splitting ratio and residual SI.

This paper considers a Q-ary orthogonal direct-sequence code-division multiple-access (DS-CDMA) system with high-rate space-time linear dispersion codes (LDCs) in time-varying Rayleigh fading multiple-input-multiple-output (MIMO) channels. We propose a joint multiuser detection, LDC decoding, Q-ary demodulation, and channel-decoding algorithm and apply the turbo processing principle to improve system performance in an iterative fashion. The proposed iterative scheme demonstrates faster convergence and superior performance compared with the V-BLAST-based DS-CDMA system and is shown to approach the single-user performance bound. We also show that the CDMA system is able to exploit the time diversity offered by the LDCs in rapid-fading channels.

Compressive sensing (CS) techniques can be used to reduce the pilot overhead, and to improve the performance of channel estimation in massive multiple-input multiple-output (MIMO) systems. Most existing methods adopt the DFT matrix as a basis, which leads to direction mismatch and energy leakage problem in practice. However, the properties of geometry-based stochastic channel model (GSCM) are usually overlooked, but can be exploited to improve the performance of channel estimation. In this paper, a multi-resolution discrimination dictionary learning (MRDDL) method is proposed for downlink sparse channel estimation in frequency-division duplexing (FDD) MIMO systems. By taking into consideration that far scatterers in a specific cell are fixed at a certain position in the space and multipath angle of arrival (AOA) from far scatterers is concentrated in a fixed range, we design a specific dictionary for each far scatterer to reduce the redundant atoms. Simulations are conducted to validate the robustness and effectiveness of the MRDDL method over existing channel estimation methods.

—This paper investigates a wireless powered intelligent radio environment, where a fractional non-linear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS) assisted wireless powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to optimally design the time durations in closed-form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the Complex Circle Manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

In this paper, filter bank based multicarrier systems using fast convolution approach are investigated. We show that exploiting offset quadrature amplitude modulation enables us to perform FFT/IFFT based convolution without overlapped processing and the circular distortion can be discarded as a part of orthogonal interference terms. This property has two advantages. Firstly, it leads to spectral efficiency enhancement in the system by removing the prototype filter transients. Secondly, the complexity of the system is significantly reduced due to using efficient FFT algorithms for convolution. The new scheme is compared with the conventional waveforms in terms of out of band radiation, orthogonality, spectral efficiency and complexity. The performance of the receiver and the equalization methods are investigated and compared with other waveforms through simulations. Moreover, based on the time variant nature of the filter response of the proposed scheme, a pilot based channel estimation technique with controlled transmit power is developed and analysed through lower bound derivations. The proposed transceiver is shown to be a competitive solution for future wireless networks.

Several widely linear equalization algorithms utilizing the rotationally variant nature of the received signals are pre sented in this paper to combat the detrimental effect of in tersymbol interference (ISI) introduced by frequency selec tive channels. Their adaptive implementations and appli cation to the time-reversal space-time coded (TR-STBC) system are also considered. In addition, a widely linear approach to turbo equalization is derived for systems em ploying error correction code. The widely linear equaliz ers and turbo equalizer are evaluated over broadband fixed wireless access channels, and are shown to yield superior performance compared to the conventional linear schemes.

Recently, the fifth-generation (5G) cellular system has been standardised. As opposed to legacy cellular systems geared towards broadband services, the 5G system identifies key use cases for ultra-reliable and low latency communications (URLLC) and massive machine-type communications (mMTC). These intrinsic 5G capabilities enable promising sensor-based vertical applications and services such as industrial process automation. The latter includes autonomous fault detection and prediction, optimised operations and proactive control. Such applications enable equipping industrial plants with a sixth sense (6S) for optimised operations and fault avoidance. In this direction, we introduce an inter-disciplinary approach integrating wireless sensor networks with machine learningenabled industrial plants to build a step towards developing this 6S technology. We develop a modular-based system that can be adapted to the vertical-specific elements. Without loss of generalisation, exemplary use cases are developed and presented including a fault detection/prediction scheme, and a sensor density-based boundary between orthogonal and non-orthogonal transmissions. The proposed schemes and modelling approach are implemented in a real chemical plant for testing purposes, and a high fault detection and prediction accuracy is achieved coupled with optimised sensor density analysis.

In this paper, we consider a 2-hop downlink point-to-multipoint fixed relay systems and propose a novel linear processing strategy at the base station and relay station to improve the reliability of the source-relay and relay-to-destination links. When coupled with a soft-decode-and-forward protocol, the proposed scheme leads to significant performance gain compared to the conventionl solutions for fixed relay networks.

Cooperative localization is an important technique in wireless networks. However, there are always errors in network node localization, which will spatially propagate among network nodes when performing network localization. In this paper, we study the spatial error propagation characteristics of network localization, in terms of Fisher information. Firstly, the spatial propagation function is proposed to reveal the spatial cooperation principle of network localization. Secondly, the convergence property of spatial localization information propagation is analyzed to shed light on the performance limits of network localization through spatial information propagation. It is shown that, (ı) the network localization error propagates in a way of the Ohm’s law in electric circuit theory, where the measurement accuracy, node location accuracy and geometric-resolution information behave like the resistances connected in parallel or series; (ıı) the network location error gradually diminishes with spatial localization cooperation procedures, due to the cooperative localization information propagation; (ııı) the essence of spatial localization cooperation among network nodes is the spatial propagation of localization information.

In this article, we consider the joint subcarrier and power allocation problem for uplink orthogonal frequency division multiple access system with the objective of weighted sum-rate maximization. Since the resource allocation problem is not convex due to the discrete nature of subcarrier allocation, the complexity of finding the optimal solution is extremely high. We use the optimality conditions for this problem to propose a suboptimal allocation algorithm. A simplified implementation of the proposed algorithm has been provided, which significantly reduced the algorithm complexity. Numerical results show that the presented algorithm outperforms the existing algorithms and achieves performance very close to the optimal solution.

A clear understanding of mixed-numerology signals multiplexing and isolation in the physical layer is of importance to enable spectrum efficient radio access network (RAN) slicing, where the available access resource is divided into slices to cater to services/users with optimal individual design. In this paper, a RAN slicing framework is proposed and systematically analyzed from a physical layer perspective. According to the baseband and radio frequency (RF) configurations imparities among slices, we categorize four scenarios and elaborate on the numerology relationships of slices configurations. By considering the most generic scenario, system models are established for both uplink and downlink transmissions. Besides, a low out of band emission (OoBE) waveform is implemented in the system for the sake of signal isolation and inter-service/slice-band-interference (ISBI) mitigation. We propose two theorems as the basis of algorithms design in the established system, which generalize the original circular convolution property of discrete Fourier transform (DFT). Moreover, ISBI cancellation algorithms are proposed based on a collaboration detection scheme, where joint slices signal models are implemented. The framework proposed in the paper establishes a foundation to underpin extremely diverse user cases in 5G that implement on a common infrastructure.

Universal filtered multi-carrier (UFMC) systems offer a flexibility of filtering arbitrary number of subcarriers to suppress out of band (OoB) emission, while keeping the orthogonality between subbands and subcarriers within one subband. However, subband filtering may affect system performance and capacity in a number of ways. In this paper, we first propose the conditions for interference-free one-tap equalization and corresponding signal model in the frequency domain for multi-user (MU) UFMC system. Based on this ideal interference-free case, impact of subband filtering on the system performance is analyzed in terms of average signal-to-noise ratio (SNR) per subband, capacity per subcarrier and bit error rate (BER) and compared with the orthogonal frequency division multiplexing (OFDM) system. This is followed by filter length selection strategies to provide guidelines for system design. Next, by taking carrier frequency offset (CFO), timing offset (TO), insufficient guard interval between symbols and filter tail cutting (TC) into consideration, an analytical system model is established. New channel equalization algorithms are proposed by considering the errors and imperfections based on the derived signal models. In addition, a set of optimization criteria in terms of filter length and guard interval/filter TC length subject to various constraints is formulated to maximize the system capacity. Numerical results show that the analytical and corresponding optimal approaches match the simulation results, and the proposed equalization algorithms can significantly improve the BER performance.

This paper presents a method to significantly reduce the preprocessing complexity of the sphere decoder (SD) in frequency-selective channels. The method consists of calculating an approximate QR decomposition (AQRD) of the channel matrix, making use of its special Toeptliz and block-Topelitz structure in single and multiple-antenna frequency-selective channels, respectively. The AQRD obtains the QR decomposition of a small submatrix of the channel matrix and extends that result to the rest of the matrix, resulting in a considerable complexity reduction compared to the original full QR decomposition (FQRD). Simulation results show that, despite the lower complexity of the AQRD, it causes only a small bit error rate (BER) performance degradation in the SD.

Cognitive satellite-terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station (BS) serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information (CSI) uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely S-Procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters.

This paper presents a novel spatial frequency domain packet scheduling and frequency domain equalization (FDE) algorithm for uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. Our analysis model is confined to 3GPP uplink SC-FDMA transmission with Multi-user (MU) Spatial Division Multiplexing (SDM). The results show that the proposed MU-MIMO scheduler in conjunction with the new FDE singificantly increases the maximum achievable rate and improves the bit error rate (BER) performance for the system under consideration.

Channel estimation schemes for fixed wireless access (FWA) multiple-input, multiple-output (MIMO) systems are considered in this study. The use of multiple antennas in combination with advanced detection techniques, such as turbo equalization is an effective means for a FWA system to provide high quality and high data rate services. Accurate knowledge, i.e., a good estimate of the underlying channel is essential for turbo equalization to achieve good performance. In this paper, we investigate some algorithms that are suitable for estimating FWA MIMO channels. The proposed schemes are evaluated and compared using different training sequences. Based on our analysis and numerical results, some recommendations are made on how to design appropriate channel estimator and how to choose training sequences for practical FWA systems