Dr Dionysia Triantafyllopoulou

In this paper we present an analytical framework that aims to improve the energy efficiency of traffic offloading via Wireless Local Area Networks, taking into account the energy consumption for both data transmission and network discovery operations. More specifically, the network scanning period is optimized in order to minimize the energy consumption in a vehicular scenario where a user moves along a road covered by a long range cellular network and a number of randomly deployed Wireless Local Area Networks. The performance of the system that performs periodic network scanning with the optimal period is compared against a sub-optimal system that does not take into consideration the user and network context information when determining the network scanning period. According to performance evaluation results, the use of the optimal network scanning period achieves significant improvement in terms of energy consumption and network detection delay.

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of uplink Long Term Evolution (LTE) systems. The proposed algorithm considers the main constraints in uplink LTE resource allocation, i.e., the allocation of contiguous sets of resource blocks of the localized Single Carrier – Frequency Division Multiple Access (SC-FDMA) physical layer to each user, and the imperfect knowledge of the users' uplink buffer status and packet waiting time. The optimal resource allocation is formulated as a discrete connected cake-cutting problem, where different agents are allocated consecutive subsequences of a sequence of indivisible items. This problem is NP-hard, therefore a suboptimal algorithm is introduced, which performs resource allocation using information on the estimated uplink packet delay, the average delay and data rate of past allocations, as well as the required uplink power per resource block. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, and fairness. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

The previous chapters mainly examined methods to save energy at the mobile handset, either by using short-range cooperation between mobile terminals, or by performing smart vertical handovers between heterogeneous radio access technologies. These techniques can be beneficial to mobile systems, but they have to be performed based on informed decisions; meaning that mobile devices need to be cognitive. Modern devices already collect significant amounts of information, but they have limited capability to exploit such context/information, and handover decisions are merely based on signal strength, or are network controlled and based on network load. In this chapter, we aim to go beyond the state-of-the-art by envisioning mobile terminals with the capability to make informed decisions based on a reservoir of context information made available through context providers; namely what is referred to as smart phones. We include a survey of the current state of the art for context extraction and management in context-aware systems; besides listing the current context extraction techniques and research efforts, we pinpoint the important properties of good context extraction techniques. Thereafter, we discuss how context information can be exploited in energy saving when performing network or node discovery mechanism, by instructing the mechanisms to scan for certain nodes/networks which are known to be in the vicinity. Finally, we discuss the range of context information that can be used to make informed decisions to save power.

The fifth-generation wireless communication networks (5G) facilitate a wide range of newly-emerging applications alongside existing cellular mobile broadband services. One of the key service classes of 5G is Ultra-Reliable and Low-Latency Communications (URLLC), which guarantees the rapid delivery of short packets (up to 1 ms) with a success probability rate of 99.999%. The challenging reliability and latency requirements of URLLC cannot be delivered by existing cellular networks, resulting in the need for significant air interface modifications. This study aims to satisfy the link latency requirements of URLLC applications, and specifically reduce the latency associated with the presence of the Hybrid Automatic Repeat reQuest (HARQ) feedback scheme. To this end, we investigate a supervised learning method to provide early HARQ (E-HARQ) feedback on the decodability status of the coded-received signal, ahead of the decoding processing. This strategy allows the transmitter to react faster and minimize the signal round-trip time (RTT). The simulation results demonstrate the capability of the proposed mechanism to speed up the feedback releasing and enhance the prediction accuracy by 12% with the introduction of a new feature derived by the channel state estimation.

In this paper we present and evaluate the performance of a routing and link scheduling algorithm for millimeter wave (mmWave) backhaul networks. The proposed algorithm models the end user behavior as being selfish, i.e., it considers users always aiming to maximize their individual utility, rather than the global optimization objective. Our system utilizes popular concepts from the economics and fairness literature. Specifically, in order to forward packets between the access points that comprise the backhaul network the Shapley value method is applied, which is shown to induce solutions with reduced latency. The performance of the proposed algorithm is evaluated in terms of the total delay, as well as the price of anarchy, which represents the inefficiency of a scheduling policy when users are allowed to adapt their rates in a selfish manner and reach an equilibrium. A relaxed version of the problem is also presented, which provides a lower bound on the value of the optimal solution. This is used for the calculation of the price of anarchy, since the problem of finding the optimal solution is NP-hard. According to simulation results, the system that employs the proposed algorithm outperforms in terms of delay and price of anarchy a system that considers a First-In-First-Out (FIFO) packet forwarding policy, as well as a system that employs local search global optimization, under which users aim at optimizing the overall delay in the network.

This deliverable presents the third and final cycle of trials and experimentation activities executed over 5GENESIS facilities. The document is the continuation of deliverables D6.1 and D6.2, in the sense that it captures tests carried out over the evolved infrastructures hosting 5GENESIS facilities following the methodology defined in the previous editions of this deliverable. The tests reported in this document focus on i) the final 5G infrastructure deployments that includes radio and core elements mostly in Stand-Alone (SA) deployment configurations based on commercial and open implementations, and ii) the various use cases/applications, some of them also involving field trials. Most of the tests described herein, especially the generic/lab ones are performed using the Open5GENESIS experimentation suite. 

In this paper we present a co-primary spectrum sharing algorithm for the Quality of Service (QoS) enhancement of uplink Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems. We consider the limitations that are resulting from the fact that each user can only be provided with only contiguous sets of resource blocks (following the constraints of the localized SC-FDMA physical layer), and the effect of the limited, or even lack of, knowledge of each user’s buffer status and packet delays in the uplink. The sharing of available resources is based on the operator spectrum access priority, an estimation of the packet delays in the uplink direction, the average delay and data rate of earlier allocations, and the power per resource block. Simulation results show that the proposed algorithm considerably improves the performance in terms of packet loss rate, goodput, and fairness.

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of uplink Long Term Evolution (LTE) systems. The proposed algorithm considers the main constraints in uplink LTE resource allocation, i.e., the allocation of contiguous sets of resource blocks of the Single Carrier – Frequency Division Multiple Access (SC-FDMA) physical layer to each user, and the imperfect knowledge of the users' uplink buffer status and packet waiting time. Resource allocation is performed using information on the estimated uplink packet delay, the average delay and data rate of past allocations, as well as the required uplink power per resource block. According to simulation results, the proposed algorithm achieves significant performance improvement in terms of packet loss rate, goodput, fairness, and energy efficiency. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

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.

The ongoing development of mobile communication networks to support a wide range of superfast broadband services has led to massive capacity demand. This problem is expected to be a significant concern during the deployment of the 5G wireless networks. The demand for additional spectrum to accommodate mobile services supporting higher data rates and having lower latency requirements, as well as the need to provide ubiquitous connectivity with the advent of the Internet of Things (IoT) sector, is likely to considerably exceed the supply, based on the current policy of exclusive spectrum allocation to mobile cellular systems. Hence, the imminent spectrum shortage has introduced a new impetus to identify practical solutions to make the most efficient use of the scarce licensed bands in a shared manner. Recently, the concept of dynamic spectrum sharing has received considerable attention from regulatory bodies and governments globally, as it could potentially open new opportunities for mobile operators to exploit spectrum bands whenever they are underutilised by their owners, subject to service level agreements. Although various sharing paradigms have been proposed and discussed, the impact and performance gains of different schemes can be scenario-specific and vary depending on the nature of the sharing parties, the level of sharing and spectrum access scheme. In this survey, we describe the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing licensed bands. Finally, we conclude this survey paper with open research challenges and suggest some future research directions.

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. The proposed algorithm performs resource allocation using information on the downlink packet delay, the average delay and data rate of past allocations, as well as the downlink users' buffer status in order to minimize packet segmentation. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, fairness, and average delay. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

In this paper we present an analytical framework to improve the energy consumption of mobile nodes through traffic offloading via Wireless Local Area Networks (WLANs), taking into account the energy consumption for both data transmission and network discovery operations. More specifically, we formulate an optimization problem, according to which the network scanning period is optimized in order to minimize the total energy consumption and the energy consumption per transmitted bit in a scenario where a user moves with a constant, either pedestrian or vehicular, speed along a road covered by a long range cellular network and a number of randomly deployed WLANs. The performance of the system that employs the proposed framework, which uses information on the user speed as well as on the availability and the load level of neighboring networks and performs periodic network scanning with the optimal period, is compared against a sub-optimal system that does not take into consideration the user and network context information when determining the network scanning period. According to performance evaluation results, the use of the optimal network scanning period achieves significant improvement in terms of total energy consumption, energy efficiency and network detection delay.

Autonomous systems and mission-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication networks capable of accommodating such applications, optimization of the air-interface characteristics is vital. This paper leverages recent advancements in the field of Artificial Intelligence (AI) technologies to optimize specific aspects of the air interface design to satisfy these stringent link reliability and latency requirements. The precise aim of this research is to reduce the link latency caused by the presence of the Hybrid Automatic Repeat reQuest (HARQ) mechanism. To this end, we propose a novel deep learning-based algorithm (Deep-HARQ), employing a deep neural network (DNN) with fully connected layers to estimate the decodability of the coded-received in-phase and quadrature (I/Q) signals prior to accomplishing the majority of the complex reception tasks. This enables the receiver to respond faster, allowing for the reduction of the signal round-trip time (RTT). To evaluate Deep-HARQ with a realistic dataset, we collected training and validation samples from a waveform compatible with 3GPP 5G NR Release 15 standards. The simulation results reveal a faster estimation response, with an accuracy enhancement of 12% compared to relevant algorithms in the literature.

Autonomous systems and mission-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication networks capable of accommodating such applications, optimization of the airinterface characteristics is vital. This paper leverages recent advancements in the field of Artificial Intelligence (AI) technologies to optimize specific aspects of the air interface design to satisfy these stringent link reliability and latency requirements. The precise aim of this research is to reduce the link latency caused by the presence of the Hybrid Automatic Repeat reQuest (HARQ) mechanism. To this end, we propose a novel deep learning-based algorithm (Deep-HARQ), employing a deep neural network (DNN) with fully connected layers to estimate the decodability of the coded-received in-phase and quadrature (I/Q) signals prior to accomplishing the majority of the complex reception tasks. This enables the receiver to respond faster, allowing for the reduction of the signal round-trip time (RTT). To evaluate Deep-HARQ with a realistic dataset, we collected training and validation samples from a waveform compatible with 3GPP 5G NR Release 15 standards. The simulation results reveal a faster estimation response, with an accuracy enhancement of 12% compared to relevant algorithms in the literature.

This paper addresses the current regulatory framework, research activities and standardization efforts towards a shared use of radio spectrum in the European Union. Two main research streams are considered: The emerging concept of Licensed Shared Access that ensures a predictable Quality of Service for secondary users of spectrum and the opportunistic Device-to-Device communications that represent a recent and enormous socio-technological trend. The paper presents also research results that support the spectrum sharing standardization path in ETSI and 3GPP.

The aim of this paper is to improve the energy efficiency during network discovery in heterogeneous networking environments. To this end, we propose a novel network discovery algorithm that exploits both user and network context information in order to efficiently adapt the network scanning period, thus avoiding unnecessary energy-consuming scanning or mis-detection of available networks that can be used as targets of handover. The performance of the proposed algorithm is compared against a system that performs network scanning in a periodic manner, without taking into consideration the user and network context information. According to simulation results, the system that employs the proposed network discovery algorithm achieves significant performance improvement in terms of energy consumption and network detection delay, with no loss in the network detection rate.

In this paper we present and evaluate the performance of a routing and link scheduling algorithm for millimeter wave backhaul networks. The proposed algorithm models the end user behavior as being selfish, i.e., it considers users always aiming to maximize their individual utility, rather than the global optimization objective. In order to forward packets through the backhaul network, the Shapley value method is applied, which is shown to induce solutions with reduced latency. The performance of the proposed algorithm is evaluated in terms of the total delay as well as the price of anarchy, which represents the inefficiency of a scheduling policy when users are allowed to adapt their rates in a selfish manner and reach an equilibrium. A relaxed version of the problem is also presented, which provides a lower bound on the value of the optimal solution. According to simulation results, the system employing the proposed algorithm outperforms in terms of delay and price of anarchy a system considering a First-In-First-Out packet forwarding policy, as well as a system employing local search global optimization, under which users aim at optimizing the overall delay in the network.

The adoption of 5G has recently picked up pace and across the globe commercial deployments are more and more numerous. In addition to better performance, 5G brings, among others, management and operation flexibility, thus allowing industry verticals to exploit features of the telecommunication networks that go far beyond the new mobile access capabilities. As with any new technology, the integration, testing and validation of new vertical applications pose great challenges at both management and operational levels. In this context, there is an evident need for 5G infrastructure facilities that offer testing and validation capabilities through a flexible experimentation framework. This paper presents the 5GENESIS EU-funded research project Experimentation Facility and the results of the validation campaigns conducted in its five experimentation platforms. The tests and obtained results were facilitated by the 5GENESIS Suite, an open-source framework providing test automation and result analytics.

The adoption of 5G has recently picked up pace and across the globe commercial deployments are more and more numerous. In addition to better performance, 5G brings, among others, management and operation flexibility, thus allowing industry verticals to exploit features of the telecommunication networks that go far beyond the new mobile access capabilities. As with any new technology, the integration, testing and validation of new vertical applications pose great challenges at both management and operational levels. In this context, there is an evident need for 5G infrastructure facilities that offer testing and validation capabilities through a flexible experimentation framework. This paper presents the 5GENESIS EU-funded research project Experimentation Facility and the results of the validation campaigns conducted in its five experimentation platforms. The tests and obtained results were facilitated by the 5GENESIS Suite, an open-source framework providing test automation and result analytics.

This book constitutes the refereed proceedings of the 14th International Conference on Cognitive Radio-Oriented Wireless Networks, CROWNCOM 2019, held in Poznan, Poland, in June 2019. The 30 revised full papers were selected from 48 submissions and present a large scope of research topic also covering IoT in 5G and how cognitive mechanisms shall help leveraging access for numerous devices; mmWave and how specific propagation and operation in these bands bring new sharing mechanisms ; how resource allocation amongst bands (including offload mechanisms) shall be solved. The key focus will be on how rich data analysis can improve the delivery of above defined services.

Research on context-aware communications has recently led to the introduction of features and algorithms relying on the presence of rich, accurate context information, and requiring however, the introduction of cross-layer information exchanges. Cognitive radio (CR), in particular, is expected to benefit from context awareness, as the cognitive engine (CE) relies on the availability of multiple information sources to operate efficiently. In this context, this work delivers a detailed, yet concise classification and description of the information exchanged in a CR network between the layers of a generic protocol stack, and between each layer and the CE. For each layer, the key services provided and delivered are presented, followed by a catalogue of exchanged parameters. The analysis, supported by a set of use cases providing a quantitative assessment of the impact of cross-layer information exchanges in a CR framework, is the basis for the discussion of key implementation challenges and the identification of the most promising partition of functions and tasks between layers and CE.

An algorithm for cooperative Dynamic Spectrum Access in Cognitive Radio networks is presented. The proposed algorithm utilizes Medium Access Control layer mechanisms for message exchange between secondary nodes that operate in license exempt spectrum bands, in order to achieve interference mitigation. A fuzzy logic reasoner is utilized in order to take into account the effect of the coexistence of a large number of users in the interference as well as to cope for uncertainties in the message exchange, caused by the nodes' mobility and the large delays in the updating of the necessary information. The proposed algorithm is applied in Filter Bank Multicarrier, as well as Orthogonal Frequency Division Multiplexing systems, and its performance is evaluated through extensive simulations that cover a wide range of typical scenarios. Experimental results indicate improved behaviour compared to previous schemes, especially in the case of uncertainties that cause underestimation of the interference levels.

In this paper, an efficient cross-layer design that performs joint adaptation of the physical (PHY) and application layers of a mobile WiMAX network is proposed. The design takes into account channel state and performance information from the PHY and medium access control (MAC) layers, respectively. It uses a decision algorithm to evaluate this information, specify unfavorable conditions regarding low channel quality and increased congestion, and take measures by coordinating modulation order, transmission power, and media encoding rate, toward improved overall quality of service (QoS) offered to the user. Extensive simulation results show that the proposed design achieves considerably reduced packet loss and power consumption, combined with increased throughput as compared to a typical mobile WiMAX system.

A cross-layer mechanism to improve the performance of real-time applications over IEEE 802.16e metropolitan area networks is presented. The proposed mechanism uses channel quality and service quality information from the physical and medium access control layers, respectively, to determine the most suitable burst profile, transmission power level and media encoding rate for a connection, or even initialize a handover execution. The main contribution of this mechanism is the integration of the handover initiation into the cross layer logic, aiming to improve the overall system performance. Extensive simulation results show that the proposed mechanism offers significant performance improvement in terms of packet loss rate, power consumption, throughput, and system capacity.

The theoretical analysis of a cross-layer mechanism for improving the quality of service of real-time applications in wireless networks is presented. The mechanism coordinates adaptations of the modulation order at the Physical layer and the media encoding mode at the Application layer, to improve packet loss rate, throughput and mean delay. With the use of Continuous Flow Modeling, the system is considered as a ``fluid'' queue with inflow and outflow rates representing its traffic generation and service rates, respectively. Each data source is modeled as a Markov chain, from the steady-state of which the optimal adaptation thresholds of the cross-layer mechanism are derived. Extensive performance evaluation results show that the optimized operation of the mechanism attains a significant performance improvement compared to both the sub-optimal case, and a legacy system, which adjusts the modulation order and encoding mode separately and independently of each other.

The theoretical analysis of a cross-layer mechanism for improving the quality of service of real-time applications in wireless networks is presented. The mechanism coordinates adaptations of the modulation order at the Physical layer and the media encoding mode at the Application layer, to improve packet loss rate, throughput and mean delay. With the use of Continuous Flow Modeling, the system is considered as a “fluid” queue with inflow and outflow rates representing its traffic generation and service rates, respectively. Each data source is modeled as a Markov chain, from the steady-state of which the optimal adaptation thresholds of the cross-layer mechanism are derived. Performance evaluation results show that the optimized operation of the mechanism attains a significant performance improvement compared to both the sub-optimal mechanism, and a legacy system.

This book aims to serve as a comprehensive technicalguide and reference material for practitioners, engineers, scientists and researchers providing them withthe state-of-the-art of research work and recentachievements in different aspects ...

Spectrum utilization and resource allocation efficiency in Cognitive Radio networks can be significantly improved by the use of efficient spectrum etiquettes implemented by appropriate spectrum policy frameworks. In such a context, dynamic and flexible policies control the behavior of the terminals and the system as a whole and allow their efficient adaptation to the continuously varying environment conditions. Policies are expressed with the use of appropriate policy description languages that guarantee the efficient communication of the various network entities and the fulfillment of the policy frameworks’ objectives. This paper is a survey of the area of policy frameworks in Cognitive Radio networks. More specifically, the characteristics, architecture and main principles of a Generic Policy Framework, are described. Moreover, an overview of the most notable policy frameworks for Cognitive Radio systems is provided.

An algorithm for power control in cooperative Cognitive Radio networks is proposed. The algorithm utilizes Medium Access Control layer mechanisms for message exchange between the nodes, in order to achieve interference mitigation. A fuzzy logic reasoner is used to cope for uncertainties that appear in real life systems, caused from parameters such as non-ideal message exchange or high user mobility. The proposed algorithm is applied in Filter Bank Multicarrier as well as Orthogonal Frequency Division Multiplexing systems under various scenarios and its performance is evaluated through extensive simulations. Experimental results indicate improved behavior compared to previous schemes, especially in the case of uncertainties that cause underestimation of the interference levels.

This paper describes the pathway towards the realisation of a 5G Facility that will allow the validation of the major 5G Key Performance Indicators (KPIs). It reflects the approach that the 5GENESIS consortium will adopt in this direction. More precisely, it describes the key design principles of such Facility as well as the targeted use cases for the KPIs validation. The adopted approach for the Facility realisation includes the design of a common implementation blueprint that will be instantiated in five Platforms distributed across Europe. To maximise the diversity and the efficiency of the Facility, complementary performance objectives have been selected for the Platforms, while specific characteristics from different vertical industries have been allocated to each of them.