Dr Juan Carlos De Luna Ducoing

Multi-user (MU)-multiple-input, multiple-output (MIMO) technology has been central to the evolution of wireless networks, since it can provide substantial network gains by enabling the concurrent transmission of a large number of information streams, over the same frequency. However, reliably detecting these mutually interfering streams comes at a very high computational cost that increases exponentially with the number of concurrently transmitted streams. This makes the corresponding MU-MIMO systems highly inefficient in terms of power consumption and processing latency. In this context, and in order to unlock the full MU-MIMO potential, alternative computing architectures are required, able to efficiently detect a large number of information streams, in a power-efficient manner. In this context, NeuroMIMO, is the first attempt to apply the principles of neuromorphic computing to achieve highly efficient MIMO detection. NeuroMIMO suggests and evaluates two different ways to translate the MIMO detection problem into a neuromorphic one. The first (i.e., Massive-NeuroMIMO) is appropriate for massive MIMO systems, where the number of receive, base-station/access-point antennas is much higher than the number of information streams. The second (i.e., Highly-Efficient-NeuroMIMO) is appropriate for the case where the number of transmitted streams approaches the number of base station antennas, and can reach the performance of the optimal Maximum-Likelihood detector. We discuss the trade-offs between the two NeuroMIMO approaches, and we show that both can provide substantial power gains compared to their traditional counterparts, while accounting for the preprocessing overhead required to translate the MIMO detection problem into a neuromorphic one. In addition, despite the current limitations in the "speed" of existing neuromorphic chips, we discuss that real-time processing detection can be achieved, even for a 5G NR system with 100 MHz operating bandwidth.

Next-generation 6G networks are expected to feature an extremely high density of network and user devices. MU-MIMO non-linear processing can provide substantially improved performance over linear processing in dense conditions, but suffers from a high complexity and processing latency. The use of the massively parallel non-linear (MPNL) processing framework can overcome such limitations. This work discusses three potential 6G transmission scenarios and evaluates their detection and precoding performance using link-level simulations and a system-level, over-the-air, 3GPP standards-based testbed. The results validate that MPNL processing has the potential to transform the way 6G MU-MIMO systems are designed.

It is well documented that the achievable throughput of MIMO systems that employ linear beamforming can significantly degrade when the number of concurrently transmitted information streams approaches the number of base-station antennas. To increase the number of the supported streams, and therefore, to increase the achievable net throughput, non-linear beamforming techniques have been proposed. These beamforming approaches are typically evaluated via simulations or via simplified over-the-air experiments that are sufficient for validating their basic principles, but they neither provide insights about potential practical challenges when trying to adopt such approaches in a standards-compliant framework, nor they provide any indication about the achievable performance when they are part of a standards-compliant protocol stack. In this work, for first time, we evaluate non-linear beamforming in a 3GPP standards- compliant framework, using our recently-proposed SWORD research platform. SWORD is a flexible, open for research, software-driven platform that enables the rapid evaluation of advanced algorithms without extensive hardware optimizations that can prevent promising algorithms from being evaluated in a standards-compliant stack. We show that in an indoor environment, vector perturbation-based non-linear beamforming can provide up to 46% throughput gains compared to linear approaches for 4×4 MIMO systems, while it can still provide gains of nearly 10% even if the number of base-station antennas is doubled.

Multiple-user, multiple-input, multiple-output (MU-MIMO) systems supporting a large number of concurrent streams have the potential to substantially improve the connectivity and throughput of future wireless communication systems. Towards this goal, deep learning (DL)-based techniques have recently been proposed for MIMO signal detection. Good performance results have been reported when compared to conventional detection methods, but it is unclear how they measure against state-of-the-art detection techniques. In this work, for the first time, we perform a critical evaluation of DetNet, MMNet, GEPNet, and RE-MIMO, four prominent model-based DL techniques based on different working principles, and assess their reliability, complexity, and robustness against the practical Massively Parallel Non-Linear processing (MPNL) detection approach. The results show that the model-based DL approaches offer promising results but have difficulty adapting to channel models that differ from those on which they were trained. They also exhibit lower reliability and higher complexity than MPNL, even without considering the training stage. We find that, at present, the human-designed MPNL outperforms the DL-based detection methods in virtually all the metrics. Nevertheless, DL-based solutions are rapidly advancing, and further research intended to address their current shortcomings may one day offer advantages over human-designed detection methods.

—This work introduces Gyre Precoding (GP), a novel linear multiuser multiple-input multiple-output (MU-MIMO) precoding approach. GP performs rotations of the symbols of each spatial layer to optimize the precoding performance. To find the rotation angles, we propose a near-optimal, gradient descent–based low-complexity algorithm. GP is constellation-agnostic and does not require significant changes to conventional receiver procedures or wireless standards. Computer evaluation results show that GP can achieve 8 dB SNR gains over linear precoding techniques and 2 dB over suboptimal symbol-level precoding (SLP) methods for a 16 × 16 MU-MIMO system. Furthermore, in a 64×12 massive-MIMO scenario in a 5G New Radio (5GNR) setup, GP achieves a 13% higher throughput gain over zero-forcing precoding. Index Terms—Multi-user multiple-input multiple-output (MU-MIMO), precoding.

Next-generation wireless networks are expected to be ultra-dense in terms of users and be able to support delay-sensitive traffic. Multiple-user, multiple-input, multiple-output (MU-MIMO) offers a potential solution by multiplexing a large number of concurrent data streams in the spatial domain. The MU-MIMO user scheduling process involves allocating the users across the space, and time or frequency resources, such that a performance metric is maximized, and subject to specific (e.g., rate) constraints being met. However, user scheduling is a combinato-rial problem, making its optimal solution highly intricate. This paper introduces the orthonormal subspace alignment scheduling (OSAS) approach, designed to be scalable for use in highly-dense networks and optimized for low-latency communications. Its design prioritizes users that align to the standard orthonormal basis and features a novel pruning process that enhances the users' transmission rates. Comparative evaluations reveal that OSAS makes more efficient use of the available resources and offers higher performance than other state-of-the-art techniques, while exhibiting lower complexity.

MIMO mobile systems, with a large number of antennas at the base-station side, enable the concurrent transmission of multiple, spatially separated information streams, and therefore, enable improved network throughput and connectivity both in uplink and downlink transmissions. Traditionally, such MIMO transmissions adopt linear base-station processing, that translates the MIMO channel into several single-antenna channels. While such approaches are relatively easy to implement, they can leave on the table a significant amount of unexploited MIMO capacity and connectivity capabilities. Recently-proposed non-linear base-station processing methods claim this unexplored capacity and promise substantially increased network throughput and connectivity capabilities. Still, to the best of the authors' knowledge, non-linear base-station processing methods not only have not yet been adopted by actual systems, but have not even been evaluated in a standard-compliant framework, involving of all the necessary algorithmic modules required by a practical system. In this work, for the first time, we incorporate and evaluate non-linear base-station processing in a 3GPP standard environment. We outline the required research platform modifications and we verify that significant throughput gains can be achieved, both in indoor and outdoor settings, even when the number of base-station antennas is much larger than the number of transmitted information streams. Then, we identify missing algorithmic components that need to be developed to make non-linear base-station practical, and discuss future research directions towards potentially transformative next-generation mobile systems and base-stations (i.e., 6G) that explore currently unexploited non-linear processing gains.

The aim of this letter is to exhibit some advantages of using real constellations in large multi-user (MU) MIMO systems. It is shown that a widely linear zero-forcing (WLZF) receiver with M-ASK modulation enjoys a spatial-domain diversity gain, which linearly increases with the MIMO size even in fully- and over-loaded systems. Using the decision of WLZF as the initial state, the likelihood ascent search (LAS) achieves nearoptimal BER performance in fully-loaded large MIMO systems. Interestingly, for coded systems, WLZF shows a much closer BER to that of WLZF-LAS with a gap of only 0:9-2 dB in SNR.

The vision, as we move to future wireless communication systems, embraces diverse qualities targeting significant enhancements from the spectrum, to user experience. Newly-defined air-interface features, such as large number of base station antennas and computationally complex physical layer approaches come with a non-trivial development effort, especially when scalability and flexibility need to be factored in. In addition, testing those features without commercial, off-the-shelf equipment has a high deployment, operational and maintenance cost. On one hand, industry-hardened solutions are inaccessible to the research community due to restrictive legal and financial licensing. On the other hand, researchgrade real-time solutions are either lacking versatility, modularity and a complete protocol stack, or, for those that are full-stack and modular, only the most elementary transmission modes are on offer (e.g., very low number of base station antennas). Aiming to address these shortcomings towards an ideal research platform, this paper presents SWORD, a SoftWare Open Radio Design that is flexible, open for research, low-cost, scalable and software-driven, able to support advanced large and massive Multiple-Input Multiple- Output (MIMO) approaches. Starting with just a single-input single-output air-interface and commercial off-the-shelf equipment, we create a software-intensive baseband platform that, together with an acceleration/ profiling framework, can serve as a research-grade base station for exploring advancements towards future wireless systems and beyond.

Conference Title: ICC 2022 - IEEE International Conference on Communications Conference Start Date: 2022, May 16 Conference End Date: 2022, May 20 Conference Location: Seoul, Korea, Republic ofMulti-user (MU), multiple-input, multiple-output (MIMO) detection has been extensively investigated, and many techniques have been proposed. However, further performance improvements may be constrained by limitations in classical computation. The motivation for this work is to test whether a machine that exploits quantum principles can offer improved performance over conventional detection approaches. This paper presents an evaluation of MIMO detection based on quantum annealing (QA) when run on an actual QA quantum processing unit (QPU) and describes the challenges and potential improvements. The evaluations show promising results in some cases, such as near-optimality in a QPSK-modulated 8×8 MIMO case, but poor results in other cases, such as for larger systems or when using 16-QAM. We show that some challenges of QA detection include dealing with integrated control errors (ICE), the limited dynamic range of QA QPUs, an exponential increase in the number of qubits to the problem size, and a high computation overhead. Solving these challenges could make QA-based detection superior to conventional approaches and bring a new generation of MU-MIMO detection methods.

In this paper, a novel approach, namely realcomplex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals. By RCHM, we mean that user terminals transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing trade-off that minimizes the required transmit-power given a sum-rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average biterror- rate as well as extensive computer simulations considering the case with single or multiple base-stations.

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