beyond-5G Networks Research Articles

Multi-Antenna Array-Based Massive MIMO for B5G/6G: State of the Art, Challenges, and Future Research Directions

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

An in-depth assessment of the physical layer performance in the proposed B5G framework

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks.

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

Navigating Criminal Responsibility in the Digital Marketplace: Implications of Network-Neutral Help Behavior and Beyond-5G Networks in E-Commerce Transactions

Navigating Criminal Responsibility in the Digital Marketplace: Implications of Network-Neutral Help Behavior and Beyond-5G Networks in E-Commerce Transactions

Symbiotic Backscatter Communication Underlying a Cell-Free Massive MIMO System

In ambient backscatter communications, backscatter devices (BDs) utilize ambient “legacy” radio signals as both a harvested energy source and a carrier on which to modulate data. Symbiotic radio, a subtype in which the legacy system assists both its own users and underlaid BDs, has attracted much research interest recently. Furthermore, cell-free massive multiple-input multiple-output (CF mMIMO) systems are promising for beyond-5G networks from both spectral and energy efficiency perspectives. To reap the benefits of both, we consider a primary CF mMIMO system where the access points (APs) aid an underlaid BD layer to both harvest energy and reflect information toward the primary receivers (which receive data from both layers). To acquire separate channel state information (CSI) of the direct and backscattered channels at the APs, a two-phase uplink pilot training method is proposed, with the effects of pilot contamination and spatial correlation between antennas accounted for. However, the receivers are assumed to only have partial CSI (statistical knowledge plus instantaneous phase information for partially-coherent reception). Assuming uplink/downlink radio channel reciprocity, the CSI is used to design downlink precoding vectors for the APs such that channel hardening is enhanced and both primary receivers and BDs benefit. We derive expressions for the average signal-to-interference-plus-noise ratios of both primary and backscatter signals, accounting for the effects of imperfect CSI, spatial correlation, pilot contamination, and channel hardening. Furthermore, the average power harvested in the BDs is derived. Simulation results demonstrate that the performance of the proposed scheme is much more uniform across all devices with CF mMIMO than with conventional co-located mMIMO. The use of CF mMIMO also largely removes the need for special consideration of the BD layer in the symbiotic system, which is not the case with co-located mMIMO.

Adaptive Reliability for the Automated Control of Human–Robot Collaboration in Beyond-5G Networks

Adaptive Reliability for the Automated Control of Human–Robot Collaboration in Beyond-5G Networks

3C Resource Sharing for Personalized Content Delivery in B5G Networks: A Contract Approach

With the emergence of numerous new applications and the explosive growth of Internet-of-Things (IoT) devices in beyond 5G (B5G) networks, the massive yet delay-sensitive personalized content delivery has imposed a crucial challenge to mobile network operators (MNOs). Cooperation among MNOs for sharing the communication, caching, and computing (3C) three-dimensional resources in an economic yet real-time manner has provided a promising solution to address this challenge. In this paper, we investigate the 3C resource sharing among multiple MNOs to realize efficiently and economically personalized content delivery, where a third-party 3C resource provider (CRP) is introduced to manage the sharing 3C resource pool. By leveraging the multi-dimensional contract theory, we propose an optimal 3C resource contract scheme for the CRP in a realistic asymmetric information scenario, and the appointed 3C resources in one contract will be allocated to the MNO who signs it. For each MNO, we establish a partial transcoding model to achieve the optimal orchestration on the 3C resources, where the closed-form solution of caching placement and transcoding strategy is obtained. Then, MNOs can choose the most suitable contracts to sign based on the service requirements from end users. In particular, we analyze the global incentive compatibility and feasibility of the proposed multi-dimensional contract approach, which is theoretically proven to achieve the optimal solution. Extensive simulation results demonstrate the efficiency of the proposed 3C resource sharing mechanism compared with other benchmark schemes. Specifically, the proposed 3C resource sharing scheme can reduce 35% delivery delay compared with the non-sharing scheme.

LLM Adaptive PID Control for B5G Truck Platooning Systems

This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments.

ENSING: Energy saving based data transmission in Internet of Drones for 3D connectivity in 6G networks

The Internet of Drones represents an emerging framework with a plethora of applications in the context of Beyond-5G networks. Drone technology uses are spreading from commercial, industrial up to military applications. Drones, a.k.a. Unmanned Aerial Vehicles (UAVs), are largely adopted in all those fields where men cannot reach or are unable to perform in a timely and efficient manner. Communications among UAVs are important to allow task coordination and network organization. However, as a drawback, UAVs are strongly affected by energy consumption that limits their flight and task accomplishment. In this paper, we address the energy consumption issue in UAV swarms and present a novel routing approach, namely ENSING, that aims to save energy consumption. Differently from previous approaches that simply reduce the number of control packets to save UAV battery level, ENSING approach selects the next-hop UAV forwarder according to energy constraints. In this way, only eligible UAVs with acceptable energy requirements can be selected in the packet forwarding, thus avoiding the involvement of those UAVs with limited battery level. Compared to other routing techniques, ENSING exhibits better performance in terms of packet delivery rate, throughput and latency.

From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies

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.

Wireless Multi-Interface Connectivity with Deep Learning-Enabled User Devices: An Energy Efficiency Perspective

Undoubtedly, future Beyond 5G (B5G) and 6G networks will be empowered by AI and Machine Learning (ML) technologies. However, current Deep Learning methods consume tremendous power, making them unsuitable to be deployed at edge Access Points (AP) or user devices. As energy efficiency will become one of the major Key Performance Indicators in 6G, there is an urge for realizing lowpower yet highly efficient Deep Learning at user devices, the most severely battery-limited entities. We analyze this issue through the problem of distributed user-to-multiple APs association optimization in B5G networks that integrate diverse wireless interfaces such as Sub-6GHz and mmWave bands. In the proposed Deep Reinforcement Learning (DRL) framework, each device is equipped by a light-weight Deep Q-Network (DQN), which enables it to self-optimize its APs and interfaces association to fulfill the stringent Quality of Service (QoS) requirements of its various applications. We analyze the energy consumption of the device DQN, and investigate key performance trade-offs in terms of sum-rate, user outage and energy efficiency, while disclosing the major power consuming elements of user DQN. Finally, crucial open research issues are identified for realizing energy-efficient AI at all network levels.

Fiber-terahertz-fiber bridge system in the 355-GHz band using a simple optical frequency comb and a photonics-enabled receiver.

This Letter demonstrates a high-speed fiber-terahertz-fiber system in the 355-GHz band using stable optical frequency comb generation and a photonics-enabled receiver. At the transmitter, a single dual-drive Mach-Zehnder modulator is employed to generate a frequency comb by driving the modulator under an optimal condition. At the antenna site, a photonics-enabled receiver consisting of an optical local oscillator signal generator, a frequency doubler, and an electronic mixer is used to downconvert the terahertz-wave signal to the microwave band. Simple intensity modulation and a direct detection method are used for transmitting the downconverted signal to the receiver over the second fiber link. To demonstrate the proof of concept, we transmitted a 16-quadrature amplitude modulation (QAM) orthogonal frequency-division multiplexing signal over a system consisting of two radio-over-fiber links and a 4 m wireless link in the 355-GHz band and achieved a line rate of 60 Gb/s. We also successfully transmitted a 16-QAM subcarrier multiplexing single-carrier signal over the system and achieved a capacity of 50 Gb/s. The proposed system can facilitate the deployment of ultra-dense small cells in high-frequency bands in beyond-5G networks.

Injecting cognitive intelligence into beyond-5G networks: A MAC layer perspective

The rapid rise of heterogeneous data traffic exposes the shortcomings of fifth-generation (5G) technology, which was initially designed to form self-organizing and self-sustaining networks to facilitate the adoption of the Internet-of-Everything (IoE). This study presents the applications and service requirements of future communication networks. This study details flexible design agreements of the Medium Access Control (MAC) layer of Beyond-5G (B5G) from the current 3rd Generation Partnership (3GPP) study and highlights the current open research issues and challenges which are yet to be optimized. To ensure that the network is self-sustaining and self-organized for B5G paradigm, an intelligent network design is required. Artificial Intelligence (AI) is revolutionizing every aspect of life, therefore, this article provides an overview of how AI plays an important role in improving future-generation communication by solving MAC-related issues.

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In this paper, we provide an overview on practical distributed EL techniques and their interplay with advanced communication optimization designs. In particular, typical performance metrics for dual-functional learning and communication networks are discussed. Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of "communications for learning" and "learning for communications." The application of EL techniques within a variety of future communication systems are also envisioned for beyond 5G (B5G) wireless networks. For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem. In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks. We also present technical challenges as well as emerging application opportunities in this field, with the aim of inspiring future research and promoting widespread developments of EL in B5G.

Problem Statement for the Developing a New Training Course «5G Smart Antennas Technologies»

Interest in smart antenna technologies is due to the development of ultra-dense networks of the fifth 5G and subsequent B5G (Beyond 5G) generations of the millimeter-wave band (MMW, millimeter-wave – mmWave), which is confirmed by the high publication activity of recent years both in the native, and in foreign scientific community. Together with scientific publications in foreign literature, textbooks in this area are published and republished. At the same time, in the native scientific and methodological literature, there is currently no textbook that reveals new approaches to studying the issues of adaptive beamforming using smart antennas in relation to ultra-dense 5G and B5G networks. In this paper, an analysis of the state of the problem in the field of intelligent antennas is carried out and a task is formulated for the development of an appropriate training course. Subject of study. The article is devoted to a contemporary review of the state of the problem in the field of smart antennas based on the analysis of Russian sources. Method. Analysis of the evolution of the development of methods and means of smart antenna technologies. Main results. The result of this work is the setting of the task for the development of a new training course "Technologies of smart antennas 5G". Practical significance. The analysis carried out serves as a rationale for the relevance and necessity of developing a new training course "Technologies of smart antennas 5G".

Performance of Non-Orthogonal Multiple Access (NOMA) Systems Over $N$-Nakagami-m Multipath Fading Channels for 5G and Beyond

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user.

Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications

Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality.

From QoS to QoE plus QoT in Beyond 5G Networks

In the Beyond 5G (B5G) era, a paradigm shift from technical Quality of Service (QoS) oriented networks to user-centric Quality of Experience (QoE) centred network architectures is expected to occur. With this development, the infusion of QoE user requirements into B5G networks will be critical to the emergence of ultra-reliable and ultra-low latency haptic-enabled Internet applications of the future. One such application which will signify the emergence of a tele-haptic Internet will be the mission-critical use case of remote robotic surgical task performance, precipitating a transition from content-based to skillset delivery networks for an augmented user experience. In extending network QoS to user focused QoE and with it, Quality of Task (QoT) dimensions, human users in a global control loop (such as robotic surgeons) will be capable of true-to-life immersive remote task performance through the manipulation of objects in real-time and across large geographical distances. In this paper, we discuss the linkages between network and user-centric QoS and QoE (with QoT) perspectives. Further, we explain the emergence of a future B5G network and haptic-enabled Internet of Skills (IoS), and draw on the example of an architecture applied to the task-sensitive mission-critical use case of tele-haptic surgery. In doing so, we conceptualise and present Task-Technology Fit (TTF) theoretical and predictive modelling that can be empirically applied to this futuristic use case for a novel QoE/QoT perspective of future B5G communication networks.

Energy-Efficient Virtual Resource Allocation of Slices in Vehicles-Assisted B5G Networks

Academia community started the research beyond 5G (B5G) while 5G systems and networks are still being landed for large-scale commercial applications. In order to enhance the agility and flexibility attributes of B5G networks, network function virtualization (NFV) and network slicing (NS) are attracting extensive research attention. Meanwhile, vehicles are promised to connect to the B5G networks so as to expand the service coverage and reach the ‘last one mile’. In this paper, we research the virtual resource allocation of slices in vehicles-assisted B5G networks. We aim at saving total energy cost of deployed slices while ensuring high slice acceptance ratio. We firstly present the system model of vehicles-assisted B5G networks, supporting both virtualization and slicing schemes. Then, we present the energy cost of vehicles-assisted B5G networks. Afterwards, we propose one energy efficient algorithm, abbreviated as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ener-Eff-Slice</i> , to solve the virtual resource allocation of slices in vehicles-assisted B5G networks. Numerical results are recorded, plotted and discussed, which prove the efficacy of our scheme. Finally, we do the conclusion marks and discuss the next-step work.