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The development of high-speed wireless communications at sub-THz frequencies is a promising solution for the continuous growth of the data traffic. At these frequencies, the main challenge is to mitigate the high propagation losses by realizing high-gain, efficient and wideband antennas. However, the fabrication constraints often limit the antenna performance, encouraging for more expensive, high-resolution technologies. Transmitarray antennas are suitable candidates, combining the advantages of spatially fed architectures with planar-based fabrication processes. In this contribution, a fast and rigorous approach for the analysis and synthesis of passive (fixed-beam) transmitarrays at 300 GHz is developed. The proposed method allows for a cost-effective design, while targeting for the maximum achievable performance. Different prototypes have been fabricated, using standard printed circuit board or quartz lithography process. The experimental results demonstrated high gain, up to 43.2 dBi, very high aperture efficiency, up to 48.6%, and wideband performance, up to more than 30% of relative bandwidth, in different prototypes. A numerical method based on physical optics for future low-profile transmitarray designs has been developed, as an alternative to the ray tracing approach model.; Le développement des communications sans fil à haut débit à des fréquences sub-THz est une solution prometteuse pour la croissance continue du trafic de données. À ces fréquences, le principal défi consiste à atténuer les pertes de propagation accrues en réalisant des antennes à gain élevé, efficaces et à large bande. Cependant, les contraintes de fabrication limitent souvent les performances de l'antenne, résultant ainsi en technologies coûteuses avec une résolution plus élevée. Les antennes à réseaux transmetteurs sont des candidats appropriés, combinant les avantages des architectures alimentées spatialement avec des processus de fabrication planaires. Dans cette contribution, une approche synthétique pour l'analyse et la synthèse des réseaux transmetteurs passifs (faisceaux fixes) à 300 GHz est développée. La méthode proposée permet une conception rentable, tout en ciblant les performances maximales réalisables. Différents prototypes ont été fabriqués, en utilisant un circuit imprimé standard ou un procédé de lithographie au quartz. Les résultats expérimentaux ont démontré un gain élevé, jusqu'à 43,2 dBi, une efficacité d'ouverture très élevée, jusqu'à 48,6%, et des performances à large bande, jusqu'à plus de 30% de la bande passante relative, dans différents prototypes. Une méthode numérique basée sur l'optique physique pour les futures conceptions des réseaux transmetteurs à profil bas a été développée, comme alternative au modèle d'approche de lancer de rayons.

Includes supplementary material. Nonorthogonal multiple access (NOMA) is recognized as an important technology to meet the performance requirements of fifth generation (5G) and beyond 5G (B5G) wireless networks. Through the technique of overloading, NOMA has the potential to support higher connection densities, increased spectral efficiency, and lower latency than orthogonal multiple access. The role of NOMA in 5G/B5G wireless networks necessitates a clear understanding of how overloading variability affects network robustness. This dissertation considers the relationship between variable overloading and network robustness through the lens of temporal network theory, where robustness is measured through the evolution of temporal connectivity between network devices (ND). We develop a NOMA temporal graph model and stochastic temporal component framework to characterize time-varying network connectivity as a function of NOMA overloading. The analysis is extended to derive stochastic expressions and probability mass functions for unidirectional connectivity, bidirectional connectivity, the inter-event time between unidirectional connectivity, and the minimum time required for bidirectional connectivity between all NDs. We test the accuracy of our analytical results through numerical simulations. Our results provide an overloading-based characterization of time-varying network robustness that is generalizable to any underlying NOMA implementation. National Security Agency, Fort George G. Meade, MD 20775 Major, United States Marine Corps Approved for public release. Distribution is unlimited.

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.

Land mobile satellite communication and physical layer security are considered as the promising paradigms in beyond 5G networks. In this paper, we establish the multi-relay hybrid satellite-terrestrial systems (HSTSs) with artificial noise (AN) to investigate the reliability and security, in which the satellite sends signals to the terrestrial destination through multiple decode-and-forward relays and there exists an eavesdropper trying to overhear the useful information. In order to improve the overall system performance, two relay selection (RS) schemes: i) optimal relay-receiver pair selection (ORRPS) and ii) suboptimal relay selection (SRS) are considered. ORRPS selects an optimal relay-receiver pair and an AN signal to maximize the corresponding signal-to-interference-plus-noise ratios of ${R_{k}} \to D\left ({{R_{k} \to E} }\right)$ and the SRS refers to selecting a relay that optimize the instantaneous channel gain ${\left |{ {{h_{R_{k}D}}} }\right |^{2}}\left ({{{{\left |{ {{h_{R_{k}E}}} }\right |}^{2}}} }\right)$ among $K$ relays, with the traditional round-robin selection scheme as a benchmark for comparison. Furthermore, the exact closed-form analytical expressions of outage probability (OP) and intercept probability (IP) under different RS schemes are derived for AN and non-AN conditions. To obtain more insights, the asymptotic analysis for high signal-to-noise-ratio regime is carried out. Based on the asymptotic analysis of OP, the diversity orders of the HSTSs are obtained. The numerical results and theoretical analysis shown that: i) The reliability of TRRS scheme is the worst among the three RS schemes, and the reliability of ORRPS scheme is the best; ii) The AN technology can significantly improve the security of the system, although it has a little adverse affect on the reliability; iii) Infrequent light shadowing has the best reliability, frequent heavy shadowing has the worst outage performance, and the change of security is opposite; iv) The OP of SRS and ORRP schemes decreased with the increase of $K$ , while the IP changed on the contrary, and the reliability and security of TRRS scheme did not change obviously.

Future networks communication scenarios by the 2030s will include notable applications are three-dimensional (3D) calls, haptics communications, unmanned mobility, tele-operated driving, bio-internet of things, and the Nanointernet of things. Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications, the future networks of the 2030s will require bandwidths in several gigahertzes (GHz) (from tens of gigahertz to 1 terahertz [THz]) to perform optimally. Based on the current radio frequency allocation chart, it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz (0.09 THz). Interestingly, these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum, specifically in the Terahertz (THz) frequency band. The major contribution of this study is discussing the substantial issues and key features of THz waves, which include (i) key features and significance of THz frequency; (ii) recent regulatory; (iii) the most promising applications; and (iv) possible open research issues. These research topics were deeply investigated with the aim of providing a specific, synopsis, and encompassing conclusion. Thus, this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.

The 6G vision of creating authentic digital twin representations of the physical world calls for new sensing solutions to compose multi-layered maps of our environments. Radio sensing using the mobile communication network as a sensor has the potential to become an essential component of the solution. With the evolution of cellular systems to mmWave bands in 5G and potentially sub-THz bands in 6G, small cell deployments will begin to dominate. Large bandwidth systems deployed in small cell configurations provide an unprecedented opportunity to employ the mobile network for sensing. In this paper, we focus on the major design aspects of such a cellular joint communication and sensing (JCAS) system. We present an analysis of the choice of the waveform that points towards choosing the one that is best suited for communication also for radar sensing. We discuss several techniques for efficiently integrating the sensing capability into the JCAS system, some of which are applicable with NR air-interface for evolved 5G systems. Specifically, methods for reducing sensing overhead by appropriate sensing signal design or by configuring separate numerologies for communications and sensing are presented. Sophisticated use of the sensing signals is shown to reduce the signaling overhead by a factor of 2.67 for an exemplary road traffic monitoring use case. We then present a vision for future advanced JCAS systems building upon distributed massive MIMO and discuss various other research challenges for JCAS that need to be addressed in order to pave the way towards natively integrated JCAS in 6G.

Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G. European Commission Horizon 2020 University College Dublin National Research Foundation of Korea Korean Government Academy of Finland Korea government

The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective and reliable power supply to the BSs, especially that sunlight is free, available everywhere, and a good alternative energy option for the remote areas. Nevertheless, planning a photovoltaic scheme necessitates viability assessment to avoid poor power supply, particularly for BSs. Therefore, cellular operators need to consider both technical and economic factors before the implementation of solar-powered BSs. This paper proposed the user-centric cell zooming policy of solar-powered cellular base stations taking into account the optimal technical criteria obtained by the HOMER software tool. The results have shown that the proposed system can provide operational expenditure (OPEX) savings of up to 47%. In addition, the efficient allocation of resource blocks (RBs) under the cell zooming technique attain remarkable energy-saving performance yielding up to 27%.

Increase in mobile network traffic led to a search for solutions to solve the higher capacity and coverage demands of users. In the meantime, technological advancement allowed the development of new technologies to fulfil increasing mobile traffic demands. To overcome these demands, a Beyond 5G scenario with user equipment taking an active role in the Radio Access Network is presented. A measurement campaign is performed using current Long Term Evolution technology, and 5G New Radio technology with a relay station to compare the performances of both technologies and assess the gains from 5G New Radio relay capabilities. Achieved benefits at the end of the measurement campaign are highly dependent on the location of the involved UEs, and the most significant gains are observed in the uplink.

This Master's Thesis is encompassed in a vision of a Beyond 5G (B5G) scenario, where the User Equipment is exploited not only to satisfy the specific needs of the user, but also to augment the Radio Access Network (RAN) infrastructure. The research work has consisted in studying and analysing the deployment of a network using UEs as relaying devices in order to achieve an augmented RAN that will be able to offer a better performance to the users, including higher spectral efficiency, and lower outage probability. The conducted studies have consisted in performing variations on the configuration parameters of the network, as well as characterising the relay nodes, by means of simulations. The obtained results have then been analysed, evaluating them in terms of spectral efficiency and outage probability, and a specific relay activation strategy has been proposed, which has proven to introduce improvements in the network performance.

International audience; The design and characterization of a 60×60element high-gain, wideband transmitarray antenna at 300 GHz are presented. A numerical model is used to accurately design the 8 phase-shifting anisotropic unit-cells of the array (3-bit phase quantization). The planar lens comprises only three metal layers and no metallized vias. It is fabricated using a low-cost, standard printed circuit board technology. A 20-dBi horn is used as a feed. The experimental characterization demonstrates a peak gain of 37.0 dBi with a maximum aperture efficiency of 48.6% and a 1-dB gain bandwidth of 19.0%. These features, combined with the cost-efficient fabrication process, make the prototype a very attractive candidate for point-to-point communications in the 300-GHz band.

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency resource. It is well known that a multi-antenna base station (BS) can efficiently serve a number of users not exceeding the number of antennas at the BS via precoding design. However, when there are more users than the number of antennas at the BS, conventional precoding design methods perform poorly because inter-user interference cannot be efficiently eliminated. In this paper, we investigate the performance of a highly-loaded multiuser system in which a BS simultaneously serves a number of users that is larger than the number of antennas. We propose a dynamic bandwidth allocation and precoding design framework and apply it to two important problems in multiuser systems: i) User fairness maximization and ii) Transmit power minimization, both subject to predefined quality of service (QoS) requirements. The premise of the proposed framework is to dynamically assign orthogonal frequency channels to different user groups and carefully design the precoding vectors within every user group. Since the formulated problems are non-convex, we propose two iterative algorithms based on successive convex approximations (SCA), whose convergence is theoretically guaranteed. Furthermore, we propose a low-complexity user grouping policy based on the singular value decomposition (SVD) to further improve the system performance. Finally, we demonstrate via numerical results that the proposed framework significantly outperforms existing designs in the literature.

Massive MIMO is one of the promising techniques to improve spectral efficiency and network performance for reaching its targeted multi-gigabit throughput in 5G systems. For 5G New Radio (NR) systems, one of the key differences compared to 4G systems is the utilization of high frequency millimeter wave (mmWave) bands in addition to sub-6GHz bands. To keep the complexity and implementation cost low, hybrid analog-digital beam-forming with large-scale antenna array has become a common design approach to address the issue of higher propagation loss as well as to improve spectral efficiency in mmWave communication in 5G NR. The 5G NR standard is designed to adapt to different beam-forming architecture and deployment scenarios. This paper provides the overview on beam management procedure according to the current 5G standardization progress. We discuss some major challenges of millimeter-wave communications encountered in the current 5G NR standard and present some expected enhancements considered for the future beyond-5G standard.

Energy efficiency is one of the key performance indicators in 5G New Radio (NR) networks targeted to support diversified use cases including enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). Trade-offs have to be carefully considered between energy efficiency and other performance aspects such as latency, throughput, connection densities and reliability. Energy efficiency is important for both user equipment (UE) side and base station side. On UE side, UE battery life has great impact on user experience. It is challenging to improve UE experience in other performance aspects without affecting battery life of 5G handsets. On the base station side, efficient network implementation is critical in both environmental and operation cost standpoints. To adapt different requirements and trade-offs, the 5G NR standard is designed to have great flexibility on network operation modes. This paper provides an overview on power saving techniques supported by 5G NR standards according to the current 5G standardization progress. It provides the 5G evolution path of the power saving techniques from the first release of 5G standard to the future beyond-5G releases. In addition to the existing standardized techniques, some major development trends of green communication and the future potential enhancements expected in the beyond-5G standards are discussed.

Non-orthogonal multiple access (NOMA) scheme, potentially enabling high spectralefficiency, is one of the key technological innovations in the 5th generation (5G)cellular networks. The massive machine type communications (mMTC) servicecategory of 5G supports ultra-dense deployment of connected devices with low-powerand low data-rate requirements, and so it naturally supports internet- of-things (IoT)communications. Also, the ambient backscatter communications (BsC) holds potentialfor enabling energy-constrained IoT communications due to its extremely low powerrequirements. This work analyzes the downlink of a NOMA-enabled cellular systembundled with symbiotic radio-based mMTC. Closed-form expressions of the outageprobability for the signal-to-interference and noise ratio (SINR) and the ergodic rate are derived for the symbiotic radio backscatter device as well as the primary cellular user.Several interesting use-cases of the proposed system model are discussed for futuremassive IoT and cell-free networks. The theoretical results presented are validatedthrough numerical evaluations.

This paper envisions to augment the Radio Access Network (RAN) infrastructure in Beyond 5G(B5G) systems by exploiting relaying capabilities of user equipment (UE) as a way to improve the coverage, capacity and robustness. Despite the concept and enabling technologies have been in place for some time, their efficient realization requires the conception and development of new features in B5G systems. Among them, this paper focuses on the Relay UE (RUE) activation decision making, in charge of deciding where and when a UE is suitable to be activated to relay traffic from other UEs. Specifically, the paper analyses seven RUE activation strategies that differ on the criteria and the type of context information considered for this decision-making problem. The considered strategies are evaluated through system level simulations in a realistic urban scenario with the objective of assessing the value of each type of context information. Results reveal that the most efficient strategies from the perspective of outage probability reduction are those that account for the number of UEs that would be served by a RUE based on the experienced spectral efficiency. This paper is part of ARTIST project (ref. PID2020-115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033.

Funding Information: The research work presented in this paper was partially supported by the European Union's Horizon 2020 Research and Innovation Program through the INSPIRE-5Gplus project under Grant No. 871808. It was also partially supported by the national key RandD program of China under Grant No.2018YFB2100400 and the national science foundation of China under Grant No.61972308 Publisher Copyright: © 2022 IEEE. | openaire: EC/H2020/871808/EU//INSPIRE-5Gplus Unmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs' locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches., Scientific and Technological Research Council of Turkey (TÜBİTAK); European Union (EU); Horizon 2020; Industrial Leadership Project

Also available on:https://researchrepository.ucd.ie/server/api/core/bitstreams/3f84bd7d-ad9c-443e-9a65-a1591d6fe563/content Abstract Network automation is a necessity in order to meet the unprecedented demand in the future networks and zero touch network architecture is proposed to cater such requirements. Closed-loop and artificial intelligence are key enablers in this proposed architecture in critical elements such as security. Apart from the arising privacy concerns, machine learning models can also face resource limitations. Federated learning is a machine learning-based technique that addresses both privacy and communication efficiency issues. Therefore, we propose a federated learning-based model incorporating the ZSM architecture for network automation. The paper also contains the simulations and results of the proposed multi-stage federated learning model that uses the UNSW-NB15 dataset.

This paper considers a mobile edge computing-enabled cell-free massive MIMO wireless network. An optimization problem for the joint allocation of uplink powers and remote computational resources is formulated, aimed at minimizing the total uplink power consumption under latency constraints, while simultaneously also maximizing the minimum SE throughout the network. Since the considered problem is non-convex, an iterative algorithm based on sequential convex programming is devised. A detailed performance comparison between the proposed distributed architecture and its co-located counterpart, based on a multi-cell massive MIMO deployment, is provided. Numerical results reveal the natural suitability of cell-free massive MIMO in supporting computation-offloading applications, with benefits over users' transmit power and energy consumption, the offloading latency experienced, and the total amount of allocated remote computational resources., Comment: Paper accepted for presentation in IEEE ICC 2022. {\copyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses. arXiv admin note: substantial text overlap with arXiv:2111.04678

Enabling network densification to support coverage-limited millimeter wave (mmWave) frequencies is one of the main requirements for 5G and beyond. It is challenging to connect a high number of base stations (BSs) to the core network via a transport network. Although fiber provides high-rate reliable backhaul links, it requires a noteworthy investment for trenching and installation, and could also take a considerable deployment time. Wireless backhaul, on the other hand, enables fast installation and flexibility, at the cost of data rate and sensitivity to environmental effects. For these reasons, fiber and wireless backhaul have been the dominant backhaul technologies for decades. Integrated access and backhaul (IAB), where along with celluar access services a part of the spectrum available is used to backhaul, is a promising wireless solution for backhauling in 5G and beyond. To this end, in this thesis we evaluate, analyze and optimize IAB networks from various perspectives. Specifically, we analyze IAB networks and develop effective algorithms to improve service coverage probability. In contrast to fiber-connected setups, an IAB network may be affected by, e.g., blockage, tree foliage, and rain loss. Thus, a variety of aspects such as the effects of tree foliage, rain loss, and blocking are evaluated and the network performance when part of the network being non-IAB backhauled is analysed. Furthermore, we evaluate the effect of deployment optimization on the performance of IAB networks. First, in Paper A, we introduce and analyze IAB as an enabler for network densification. Then, we study the IAB network from different aspects of mmWave-based communications: We study the network performance for both urban and rural areas considering the impacts of blockage, tree foliage, and rain. Furthermore, performance comparisons are made between IAB and networks of which all or part of small BSs are fiber-connected. Following the analysis, it is observed that IAB may be a good backhauling solution with high flexibility and low time-to-market. The second part of the thesis focuses on improving the service coverage probability by carrying out topology optimization in IAB networks focusing on mmWave communication for different parameters, such as blockage, tree foliage, and antenna gain. In Paper B, we study topology optimization and routing in IAB networks in different perspectives. Thereby, we design efficient Genetic algorithm (GA)-based methods for IAB node distribution and non-IAB backhaul link placement. Furthermore, we study the effect of routing in the cases with temporal blockages. Finally, we briefly study the recent standardization developments, i.e., 3GPP Rel-16 as well as the Rel-17 discussions on routing. As the results show, with a proper planning on network deployment, IAB is an attractive solution to densify the networks for 5G and beyond. Finally, we focus on improving the performance of IAB networks with constrained deployment optimization. In Paper C, we consider various IAB network models while presenting different algorithms for constrained deployment optimization. Here, the constraints are coming from either inter-IAB distance limitations or geographical restrictions. As we show, proper network planning can considerably improve service coverage probability of IAB networks with deployment constraints.

This paper envisages a Beyond 5G (B5G) Radio Access Network (RAN) in which the relaying capabilities offered by user equipment (UE) are used as a way to improve the coverage and robustness of the network. The paper proposes and develops the functional framework for supporting the activation of the suitable relay UEs (RUEs) in coverage constrained scenarios. It is based on characterizing each potential RUE through a utility metric that measures the coverage enhancements brought to the network when the RUE is activated. To derive this metric for all the candidate RUEs, the framework considers the use of a Network Digital Twin that allows the offline analysis of different configurations in a fast and safe way. Using the proposed framework, a RUE activation algorithm is proposed and evaluated. The obtained results reflect that significant outage probability reductions can be obtained in the scenario under different traffic distributions thanks to the activation of the RUEs with the highest utility. This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033. The work is also funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union - NextGenerationEU under project OPTIMAIX_NDT (Ref. TSI-063000-2021-35).

Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.

Along the successive generations, mobile network infrastructure deployments have required continuous expansion and upgrades to respond to the ever increasing traffic demand. At the same time, user equipment (UE) has evolved with the introduction of powerful and advanced communication and computational capabilities. Moreover, several industrial initiatives are promoting the principles of RAN disaggregation through the split of RAN functionalities among multiple interoperable components in contrast to traditional monolithic solutions. Embracing these trends, this paper presents the vision of a Beyond 5G (B5G) scenario where the UE takes a more active role in the communication service provisioning and is used to augment the RAN infrastructure as a source of distributed capacity and network intelligence. This is expected to bring benefits for Mobile Network Operators by reducing the number of base stations to deploy and by providing the RAN with a new degree of flexibility for smartly and dynamically adapting the RAN to the specific needs. In this direction, after discussing the main enabling technologies of the envisaged RAN augmentation concept, this paper presents a simulation-based assessment to quantify the performance improvements and the achievable savings in terms of infrastructure deployment. This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033.

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.

Network Function Virtualization (NFV) is one of the main enablers behind the promised improvements in the Fifth Generation (5G) networking era. Thanks to this concept, Network Functions (NFs) are evolving into software components (e.g., Virtual Network Functions (VNFs)) that can be deployed in general-purpose servers following a cloud-based approach. In this way, NFs can be deployed at scale, fulfilling a great variety of service requirements. Unfortunately, the complexity in the management and orchestration of NFV-based networks has increased due to the diverse demands from a growing number of network services. Such complexity calls for an automated and autonomous solution that self adapts to the needs of those network services. In this paper, we propose and compare a Deep Reinforcement Learning (DRL) agent, a classical Proportional–Integral–Derivative (PID) controller, and a Threshold (THD)-based algorithm for autonomously determining the amount of VNF instances to fulfill a service latency requirement without knowing or predicting the expected demand. Finally, we present a comparison of the three approaches in terms of created VNFs and peak latency performed in a discrete event simulator., This research was partially funded by Ctrl App, a research project of the Fund for Scientific Research Flanders (FWO) under grant agreement No. G055619N, and by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 101017109 (DAEMON).

Funding Information: ACKNOWLEDGMENT This work was partially supported by the European Union’s Horizon 2020 Research and Innovation Program through the 5G!Drones Project under Grant No. 857031. It was also supported in part by the Academy of Finland 6Genesis project under Grant No. 318927. Publisher Copyright: © 2021 IEEE. | openaire: EC/H2020/857031/EU//5G!Drones This paper investigates the fundamental connection steering issue in cellular-enabled Unmanned Aerial Vehicles (UAVs), whereby a UAV steers the cellular connection across multiple Mobile Network Operators (MNOs) for ensuring enhanced Quality-of-Service (QoS). We first formulate the issue as an optimization problem for minimizing the maximum outage probability. This is a nonlinear and nonconvex problem that is generally difficult to be solved. To this end, we propose a new approach for solving the optimization problem based on Deep Reinforcement Learning (DRL), considering two important reinforcement learning algorithms (i.e., Deep Q-Learning (DQN) and Advantage Actor Critic (A2C)). Simulation results show that under the proposed approach, the UAVs can make optimal decisions to select the most suitable connection with MNOs for achieving the minimization of the maximum outage probability. Furthermore, the results also show that in our new approach, the A2C-based algorithm is better than the DQN-based one, especially when the number of MNOs increases, while the DQN-based algorithm can be executed in a shorter time.

Publisher Copyright: IEEE | openaire: EC/H2020/871780/EU//MonB5G As a critical enabler of the next generation networks, network slicing can dynamically and flexibly create virtual networks leveraging necessary network resources for services with different quality of service (QoS) requirements. To satisfy the strict QoS requirements of some vertical industries, e.g., industry automation, the Deterministic Network (DetNet) concept has been recently proposed to investigate deterministic service provisioning with bounds on service latency, loss, and jitter. A critical issue for providing deterministic services is to determine the right amount of resources for a network slice to ensure the QoS requirements. In this paper, using the stochastic network calculus (SNC) method, we first obtain the amount of network resource needed for a network slice to ensure an end-to-end latency requirement under specific traffic demands. We then study the network slice deployment problem in an IP-over-WDM (wavelength division multiplexing) metro aggregation networks and propose a heuristic with three different objectives: minimizing traffic hops, minimizing lightpaths and minimizing wavelengths, which can help network service provider to optimize network deployment under different considerations. The simulation results show the comparison of the resource utilization under different strategies.

This paper presents a beyond 5G fronthaul network with dynamic beamforming and -steering. The proposed fronthaul solution deploys optical beamforming (OBF) by combining space division multiplexing (SDM), analogue radio-over-fiber (ARoF), and the novel optical beam forming network (OBFN) technologies. From the service management and orchestration (MANO) point of view, the proposed fronthaul solution also deploys an advanced software defined networking (SDN) and Network Function Virtualization (NFV) control and orchestration architecture developed with the goal to optimally manage and reconfigure the physical layer resources (i.e., optical and radio) at the central office and cell sites (i.e., pool of baseband units (BBUs), remote radio heads (RRHs), ARoF transceivers and OBFNs). The proposed beyond 5G fronthaul architecture is primarily oriented to deploy massive machine-type communication (mMTC) services with high-bandwidth requirements, such as for industry 4.0. In this paper we experimentally validate the novel OBFN system, and the dynamic SDN/NFV MANO of the transport connectivity and network services for optical beamforming. The obtained experimental results show that the overall delay for the provisioning and removal of an OBF service, considering the contribution of the involved optical and radio systems and the SDN/NFV MANO layer, is 134s and 18s respectively. The reconfiguration of the OBF service to add or remove a beam can be performed in the range of 65–87s.

Reconfigurable intelligent surface (RIS)-empowered communication is an emerging technology that is able to effectively reshape the wireless environment into the desired form. This promising technology appears as a remedy for a wide range of wireless applications including physical layer (PHY) security. In this paper, we consider the use of RISs for a challenging PHY security scenario with two kinds of adversaries, where a warden aims to detect and an eavesdropper aims to sniff the legitimate communication. Our objective is to maximize the secrecy rate under a covert constraint. For this purpose, a sub-optimal optimization algorithm is proposed where transmit beamforming and the RIS phase shift vectors are alternately optimized. The secrecy rate performance of the proposed algorithm is investigated with computer simulations for Rician fading channels. Our results indicate that the use of RISs significantly improves the secure communication rate of a wireless system without waking the warden., NA

Reconfigurable intelligent surface (RIS)-assisted communications is one of the promising candidates for next generation wireless networks by controlling the propagation environment dynamically. In this study, a channel modeling strategy for RIS-assisted wireless networks is introduced in sub-6 GHz bands by considering both far-field and near-field behaviours in transmission. We also proposed an open-source physical channel simulator for sub-6 GHz bands where operating frequency, propagation environment, terminal locations, RIS location and size can be adjusted. It is demonstrated via extensive computer simulations that an improved achievable rate performance is obtained in the presence of RISs for both near-field and far-field conditions., NA

The Tactile Internet (TI) is an emerging area of research involving 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the Internet between tactile ends, with audio-visual data as feedback. This emerging TI technology is viewed as the next evolutionary step for the Internet of Things (IoT) and is expected to bring about a massive change in Healthcare 4.0, Industry 4.0 and autonomous vehicles to resolve complicated issues in modern society. This vision of TI makes a dream into a reality. This article aims to provide a comprehensive survey of TI, focussing on design architecture, key application areas, potential enabling technologies, current issues, and challenges to realise it. To illustrate the novelty of our work, we present a brainstorming mind-map of all the topics discussed in this article. We emphasise the design aspects of the TI and discuss the three main sections of the TI, i.e., master, network, and slave sections, with a focus on the proposed application-centric design architecture. With the help of the proposed illustrative diagrams of use cases, we discuss and tabulate the possible applications of the TI with a 5G framework and its requirements. Then, we extensively address the currently identified issues and challenges with promising potential enablers of the TI. Moreover, a comprehensive review focussing on related articles on enabling technologies is explored, including Fifth Generation (5G), Software-Defined Networking (SDN), Network Function Virtualisation (NFV), Cloud/Edge/Fog Computing, Multiple Access, and Network Coding. Finally, we conclude the survey with several research issues that are open for further investigation. Thus, the survey provides insights into the TI that can help network researchers and engineers to contribute further towards developing the next-generation Internet.