beyond-5G Networks Research Articles

Green internet of things using UAVs in B5G networks: A review of applications and strategies

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.

Edge Intelligent Networking Optimization for Internet of Things in Smart City

Massive devices connected through fifth-generation (5G) networks constitute a ubiquitous Internet of Things (IoT), providing diverse service applications in a smart city. A robust network topology structure against cyber-attacks is the foundation of highly reliable service quality, especially in next-generation networks or beyond 5G (B5G) networks. Existing methods apply neural networks with deep reinforcement learning methods to advance the network topology. However, the reduction of unique hardware resource constraints and the application of edge intelligent networking capability of terminal nodes are emerging challenges for robustness optimization of IoT with 5G and B5G networks. To address these problems, we design a distributed learning framework utilizing edge intelligence, improving smart terminal nodes' networking capability, which is deployed on ordinary computers instead of specialized hardware such as GPUs. The proposed framework leveraging multi-core CPU and intelligent edge methods decreases the training time and economic cost and takes full advantage of computer resources. Furthermore, the best performing framework considers the distributed communication model of edge computing and optimizes the network topology by taking advantage of smart terminal nodes' contributions. We show that the framework succeeds in various topologies and outperforms compared with other state-of-art algorithms in improving the robustness for IoT topology in smart cities.

Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems

Integrating artificial intelligence (AI) with mobile edge computing, edge intelligence (EI) has emerged as a new paradigm for 5G and beyond 5G (B5G) systems. The integration of EI and heterogeneous networks (e.g., mobile and wireless local area networks) also raises new concerns about security and privacy. This article examines two important security aspects of EI-empowered, heterogeneous, B5G networks, that is, authentication and trust-evaluation-based compromised user equipment (UE) detection. Technical challenges are discussed. A new edge-computing-enabled, unified authentication framework is developed, which authenticates UEs consistently via heterogeneous networks with UEs' privacy preserved. A new trust-evaluation-based compromised UE detection method is developed based on reinforcement learning to prevent compromised UEs from launching internal attacks. Case studies show that the new frameworks improve the authentication efficiency and detection rate of compromised UEs, as compared to technologies specified by current 5G standards. The frameworks have great potential to secure B5G in future heterogeneous network environments.

Towards Monolithic Indium Phosphide (InP)-Based Electronic Photonic Technologies for beyond 5G Communication Systems

This review paper reports the prerequisites of a monolithic integrated terahertz (THz) technology capable of meeting the network capacity requirements of beyond-5G wireless communications system (WCS). Keeping in mind that the terahertz signal generation for the beyond-5G networks relies on the technology power loss management, we propose a single computationally efficient software design tool featuring cutting-edge optical devices and high speed III–V electronics for the design of optoelectronic integrated circuits (OEICs) monolithically integrated on a single Indium-Phosphide (InP) die. Through the implementation of accurate and SPICE (Simulation Program with Integrated Circuit Emphasis)-compatible compact models of uni-traveling carrier photodiodes (UTC-PDs) and InP double heterojunction bipolar transistors (DHBTs), we demonstrated that the next generation of THz technologies for beyond-5G networks requires (i) a multi-physical understanding of their operation described through electrical, photonic and thermal equations, (ii) dedicated test structures for characterization in the frequency range higher than 110 GHz, (iii) a dedicated parameter extraction procedure, along with (iv) a circuit reliability assessment methodology. Developed on the research and development activities achieved in the past two decades, we detailed each part of the multiphysics design optimization approach while ensuring technology power loss management through a holistic procedure compatible with existing software tools and design flow for the timely and cost-effective achievement of THz OEICs.

Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellations

With numerous ongoing deployments owned by private companies and startups, dense satellite constellations deployed in low Earth orbit (LEO) will play a major role in the near future of wireless communications. In addition, the 3rd Generation Partnership Project (3GPP) has ongoing efforts to integrate satellites into 5G and beyond-5G networks. Nevertheless, numerous challenges must be overcome to fully exploit the connectivity capabilities of satellite constellations. These challenges are mainly a consequence of the low capabilities of individual small satellites, along with their high orbital speeds and small coverage due to the low altitude of deployment. In particular, inter-plane inter-satellite links (ISLs), which connect satellites from different orbital planes, are greatly dynamic and may be considerably affected by the Doppler shift. In this paper, we present a framework and the corresponding algorithms for the dynamic establishment of the inter-plane ISLs in LEO constellations. Our results show that the proposed algorithms increase the sum of rates in the constellation 1) by up to 115% with respect to the state-of-the-art benchmark schemes in an interference-free environment and 2) by up to 71% when compared to random resource allocation in a worst-case scenario for interference.

Composite and efficient DDoS attack detection framework for B5G networks

Distributed denial-of-service (DDoS) remains an ever-growing problem that has affected and continues to affect a host of web applications, corporate bodies, and governments. With the advent of fifth-generation (5G) network and beyond 5G (B5G) networks, the number and frequency of occurrence of DDoS attacks are predicted to soar as time goes by, hence there is a need for a sophisticated DDoS detection framework to enable the swift transition to 5G and B5G networks without worrying about the security issues and threats. A range of schemes has been deployed to tackle this issue, but along the line, few limitations have been noticed by the research community about these schemes. Owing to these limitations/drawbacks, this paper proposes a composite and efficient DDoS attack detection framework for 5G and B5G. The proposed detection framework consists of a composite multilayer perceptron which was coupled with an efficient feature extraction algorithm and was built not just to detect a DDoS attack, but also, return the type of DDoS attack it encountered. At the end of the simulations and after testing the proposed framework with an industry-recognized dataset, results showed that the framework is capable of detecting DDoS attacks with a high accuracy score of 99.66% and a loss of 0.011. Furthermore, the results of the proposed detection framework were compared with their contemporaries.

Performance Investigation of NOMA Versus OMA Techniques for mmWave Massive MIMO Communications

The fifth-generation (5G) of cellular technology is currently being deployed over the world. In the next decade of mobile networks, beyond 5G (B5G) cellular networks with the under-development advanced technology enablers are expected to be a fully developed system that could offer tremendous opportunities for both enterprises and society at large. B5G in more ambitious scenarios will be capable to facilitate much-improved performance with the significant upgrade of the key parameters such as massive connectivity, ultra-reliable and low latency (URLL), spectral efficiency (SE) and energy efficiency (EE). Equipping non-orthogonal multiple access (NOMA) with other key drivers will help to explore systems’ applicability to cover a wide variety of applications to forge a path for future networks. NOMA empowers the networks with seamless connectivity and can provide a secure transmission strategy for the industrial internet of things (IIoT) anywhere and anytime. Despite being a promising candidate for B5G networks a comprehensive study that covers operating principles, fundamental features and technological feasibility of NOMA at mmWave massive MIMO communications is not available. To address this, a simulation-based comparative study between NOMA and orthogonal multiple access (OMA) techniques for mmWave massive multiple-input and multiple-output (MIMO) communications is presented with performance discussions and identifying technology gaps. Throughout the paper, aspects of operating principles, fundamental features and technological feasibility of NOMA are discussed. Also, it is demonstrated that NOMA not only has good adaptability but also can outperform other OMA techniques for mmWave massive MIMO communications. Some foreseeable challenges and future directions on applying NOMA to B5G networks are also provided

Reliability Optimization in Narrowband Device-to-Device Communication for 5G and Beyond-5G Networks

The 5G and beyond-5G (B5G) is expected to be a key enabler for Internet-of-Everything (IoE). The narrowband Internet of Things (NB-IoT) is a low-power wide-area enabling technology introduced by the 3rd Generation Partnership in 5G. The objective of the NB-IoT is to enhance the mobile coverage area by increasing the number of repetitions of control and data packets between user equipment (UE) and the base station/evolved NodeB (BS/eNB). While these repetitions improve data delivery for delay-sensitive applications, they degrade the efficiency of the already resource-constrained IoT system by increasing the system overhead and energy consumption. Moreover, NB-IoT devices in the edge region of the cellular coverage area require more repetitions, which augment energy consumption. In this study, we investigate device-to-device (D2D) communication for NB-IoT delay-sensitive applications, such as healthcare-IoT services, to use two-hop communication instead of using a direct uplink. An optimization problem is formulated to achieve an optimal end-to-end delivery ratio (EDR). In addition, this study incorporates Q-Learning-based reinforcement learning (RL) for the selection of an optimal cellular relay, which assists NB-IoT UE in uploading sensitive data to BS/eNB. The proposed RL-intelligent-D2D (RL-ID2D) communication methodology selects the optimum relay with a maximum EDR, which ultimately augments energy efficiency.

Physical-Layer Security in 6G Networks

The sixth generation (6G) of mobile network will be composed by different nodes, from macro-devices (satellite) to nano-devices (sensors inside the human body), providing a full connectivity fabric all around us. These heterogeneous nodes constitute an ultra dense network managing tons of information, often very sensitive. To trust the services provided by such network, security is a mandatory feature by design. In this scenario, physical-layer security (PLS) can act as a first line of defense, providing security even to low-resourced nodes in different environments. This paper discusses challenges, solutions and visions of PLS in beyond-5G networks.

Neighbor Discovery for ProSe and V2X Communications

Device-to-Device (D2D) communication underlying cellular networks was first introduced in the 3GPP Rel. 12 specifications, and was initially referred to as proximity services (ProSe). Its primary aim was to serve billions of Internet of Things (IoT) devices for 5G and beyond-5G networks. To enable D2D link establishment between various user equipments (UEs), the neighbor discovery process became crucial. This article investigates neighbor discovery for ProSe and Vehicle-to-Everything (V2X) communications through a SideLink interface, which is specifically introduced to support D2D communications over cellular networks. A single-user scenario is first considered to derive the probability of discovery in its closed-form and compare it with simulation results to validate its theoretical analysis. This scenario employs the demodulation reference signal (DMRS), where a power-normalized-correlation (PNC)-based metric is performed to determine the presence of active peers in the vicinity. Moreover, a multiuser scenario is considered to assess the impact of interference on the discovery probability in the cases of low and high vehicular mobility channel models. Then, group discovery is investigated using two strategies: 1) a distributed scheme incorporating out-of-coverage communications (modes 2 and 4) or 2) a network-assisted scheme applicable for supervised communications (modes 1 and 3). In this study, discovery periods are modeled as an Aloha-like protocol in the first case and a Polling-like protocol in the second case with either MAC layer or PHY layer collision models. Simulations are performed to evaluate the time required for group discovery completion as well as the collision rate in both low-mobility and high-mobility channels.

On the Downlink Performance of RSMA-Based UAV Communications

The use of unmanned aerial vehicles (UAVs) as base stations (BSs) is envisaged as a key enabler for the fifth generation (5G) and beyond-5G networks. Specifically, aerial base stations (UAV-BS) are expected to provide ubiquitous connectivity and high spectral efficiency. To this end, we present in this correspondence an in-depth look into the integration of rate-splitting multiple access (RSMA) with UAV-BSs and downlink transmissions. A non-convex problem of joint UAV placement, RSMA precoding, and rate splitting, aiming to maximize the weighted sum data rate of users is formulated. Due to its complexity, two sub-problems are investigated, namely the UAV placement and RSMA parameters optimization. The resulting solutions are then combined to propose a novel alternating optimization method. Simulation results illustrate the latter's efficiency compared to baseline approaches.

Intelligent Ubiquitous Network Accessibility for Wireless-Powered MEC in UAV-Assisted B5G

Providing the ubiquitous network accessibility is the key goal for 5G and Beyond 5G (B5G) networks. As the number of devices and data increase, ensuring the Quality of Service(QoS) by the existing network with a singular focus could be challenging. Meanwhile, it always is of the utmost importance to perform the computation-intensive or delay-sensitive tasks welland provide long-term services for the B5G networks. To tackle these challenges, we present an air-ground integrated network in B5G wireless communications, where an UAV is deployed as an aerial radio access platform to formulate system strategy intelligently, as well as provide task offloading and energy harvesting opportunities for terrestrial devices. To get more insight of it, we propose an intelligent charging-offloading scheme and formulate the joint multi-taskcharging-offloading scheduling as an optimization problem aiming to minimizing the system servicelatency of all devices by jointly optimizing the task offloading decisions, connection scheduling, charging and computation resources allocation of UAV. However, the formulated optimization problem is a Mixed-Integer Nonlinear Programming (MINLP) problem which is challenging to solve in general. Therefore, we decompose it into multiple convex sub-problems based on Block-Coordinate Descent (BCD) method and develop an improved greedy algorithm to obtain a feasible optimal solution. To validate the proposed algorithm, it is comprehensively compared with several existing schemes. Performance evaluation demonstrates that our scheme outperforms the benchmarks in terms of the system service latency of all UDs. Moreover, we represent the system working process and the paradigm of industrial applications.

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.

Guest editorials: P2P computing for 5G, beyond 5G (B5G) networks and internet-of-everything (IoE)

Due to the technology advancements, in recent days, most of the wireless devices are connected to machines (IoT) rather than human being, will lead to the growth in Smart Cities, Smart Homes, Smart Vehicles etc. Hence, the future customers will request for higher capacity, higher data rate with global connectivity. Hence, 5G and B5G networks encounter fundamental challenges for peer-to-peer IoT communications, including automated vehicle, robotic and other automatic systems, and computing infrastructures. Therefore Internet-of-Everything (IoE), integration of everything in a single hub is the related development. Other challenges predicted for future networks are to handle Ultra-dense cell networks, Reconfigurable Hardware, Networked VLC, Networking Intelligence and technological developments to empower the users with 100% Immersive Experiences.

I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis

Physical layer security is known as a promising paradigm to ensure secure performance for the future beyond 5G (B5G) networks. In light of this fact, this paper elaborates on a tractable analysis framework to evaluate the reliability and the security of wireless-powered decode-and-forward (DF) multi-relay networks. More practical, the nonlinear energy harvesters, in-phase and quadrature-phase imbalance (IQI) and channel estimation errors (CEEs) are taken into account. To further enhance the secure performance, two relay selection strategies are presented: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) suboptimal relay selection (SRS); 2) optimal relay selection (ORS)</i> . Specifically, exact analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived in closed-form. For the IP, we consider that the eavesdropper can wiretap the signal from the source or the relay. In order to obtain more deep insights, we carry out the asymptotic analysis as well as the diversity orders for the OP in the high signal-to-noise ratio (SNR) regimes. Numerical results show that: 1) Although the mismatches of amplitude/phase of transmitter (TX)/receiver (RX) limit the OP performance, it can enhance IP performance; 2) Large number of relays yields better OP performance; 3) There are error floors for the OP due to the CEEs; 4) There is a trade-off for the OP and IP to obtain the balance between reliability and security.

Performance of Distributed Massive MIMO and Small-Cell Systems Under Hardware and Channel Impairments

Deploying a large number of distributed access points (AP)s to serve a smaller number of users is one of the promising network architectures for future wireless communication systems. Such network architecture can be operated as a distributed massive multiple-input multiple-output (MIMO) system or as a small-cell (SC) system to satisfy the anticipated high rate requirements for beyond-5G networks. However, in a practical scenario with non-ideal hardware components and high-velocity users, the network experiences an inevitable performance degradation. This paper aims at analyzing the network performance under the operation of distributed massive MIMO and SC systems, taking into account the impairments of real and dynamic systems. Considering multiple-antennas APs, we derive novel closed-form expressions for the downlink (DL) spectral efficiency of both systems. We reveal that limiting the number of served users per AP in distributed massive MIMO systems leads to a corresponding loss in the performance, especially under max-min power control. Besides, despite the SC system provides the largest per-user average DL rate under the deployment of ideal APs, distributed massive MIMO systems become superior in the presence of non-ideal APs. Notably, while increasing the number of deployed non-ideal APs can reduce the introduced loss in distributed massive MIMO systems, this leads to an extra performance loss in SC systems. Finally, we show that the presence of high-velocity users is more harsh in SC systems. In addition, our results show that the SC system operation is more suitable for low-velocity users, however, it is better to operate networks with high velocities users under the distributed massive MIMO systems operation.

Artificial Intelligence for 5G and Beyond 5G: Implementations, Algorithms, and Optimizations

The communication industry is rapidly advancing towards 5G and beyond 5G (B5G) wireless technologies in order to fulfill the ever-growing needs for higher data rates and improved quality-of-service (QoS). Emerging applications require wireless connectivity with tremendously increased data rates, substantially reduced latency, and growing support for a large number of devices. These requirements pose new challenges that can no longer be efficiently addressed by conventional approaches. Artificial intelligence (AI) is considered as one of the most promising solutions to improve the performance and robustness of 5G and B5G systems, fueled by the massive amount of data generated in 5G and B5G networks and the availability of powerful data processing fabrics. As a consequence, a plethora of research on AI-based communication technologies has emerged recently, promising higher data rates and improved QoS with affordable implementation overhead. In this overview paper, we summarize the state-of-the-art of AI-based 5G and B5G techniques on the algorithm, implementation, and optimization levels. We shed light on the advantages and limitations of AI-based solutions, and we provide a summary of emerging techniques and open research problems.

Artificial Intelligence for 5G and Beyond 5G: Implementations, Algorithms, and Optimizations

The communication industry is rapidly advancing towards 5G and beyond 5G (B5G) wireless technologies in order to fulfill the ever-growing needs for higher data rates and improved quality-of-service (QoS). Emerging applications require wireless connectivity with tremendously increased data rates, substantially reduced latency, and growing support for a large number of devices. These requirements pose new challenges that can no longer be efficiently addressed by conventional approaches. Artificial intelligence (AI) is considered as one of the most promising solutions to improve the performance and robustness of 5G and B5G systems, fueled by the massive amount of data generated in 5G and B5G networks and the availability of powerful data processing fabrics. As a consequence, a plethora of research on AI-based communication technologies has emerged recently, promising higher data rates and improved QoS with affordable implementation overhead. In this overview paper, we summarize the state-of-the-art of AI-based 5G and B5G techniques on the algorithm, implementation, and optimization levels. We shed light on the advantages and limitations of AI-based solutions, and we provide a summary of emerging techniques and open research problems.

Efficient compact modelling of UTC-photodiode towards terahertz communication system design

Monolithic optoelectronic integrated circuits, OEICs are seen as key enabling technologies to minimal power loss criteria. Monolithic OEICs combine, on the same die, cutting-edge optical devices and high speed III-V electronics able to generate terahertz signal targeting beyond-5G networks. Computationally efficient compact models compatible with existing software tool and design flow are essential for timely and cost-effective OEIC achievement. The analog nature of photonic devices wholly justifies the use of methodologies alike the ones employed in electronic design automation, through implementation of accurate (and SPICE-compatible) compact models. This multidisciplinary work, describes an efficient compact model for Uni-Traveling Carrier photodiodes (UTC PD) which is a key component for OEICs. Its equations feature the UTC PD electronic transport and frequency response along with its photocurrent under applied optical power. It also dynamically takes into account the device junction temperature, accounting for the self-heating effect. Excellent agreement between model and measurements as well as model scalability (several geometries have been validated) has been achieved that marks the first demonstration of a multi-physics, computationally efficient and versatile compact model for UTC-PDs.

One Step Greener: Reducing 5G and Beyond Networks’ Carbon Footprint by 2-Tiering Energy Efficiency with CO2 Offsetting

Fifth generation (5G) and Beyond-5G (B5G) will be characterized by highly dense deployments, both on network plane and user plane. Internet of Things, massive sensor deployments and base stations will drive even more energy consumption. User behavior towards mobile service usage is witnessing a paradigm shift with heavy capacity, demanding services resulting in an increase of both screen time and data transfers, which leads to additional power consumption. Mobile network operators will face additional energetic challenges, mainly related to power consumption and network sustainability, starting right in the planning phase with concepts like energy efficiency and greenness by design coming into play. The main contribution of this work is a two-tier method to address such challenges leading to positively-offset carbon dioxide emissions related to mobile networks using a novel approach. The first tier contributes to overall power reduction and optimization based on energy efficient methods applied to 5G and B5G networks. The second tier aims to offset the remaining operational power usage by completely offsetting its carbon footprint through geosequestration. This way, we show that the objective of minimizing overall networks’ carbon footprint is achievable. Conclusions are drawn and it is shown that carbon sequestration initiatives or program adherence represent a negligible cost impact on overall network cost, with the added value of greener and more environmentally friendly network operation. This can also relieve the pressure on mobile network operators in order to maximize compliance with environmentally neutral activity.