The Mobile Internet of Things (MIoT) represents a significant evolution of traditional IoT by enabling seamless connectivity for mobile devices and sensors in dynamic environments. Given the resource constraints and mobility challenges in MIoT networks, developing adaptive and energy-efficient routing strategies is important. This paper proposes a novel routing protocol that integrates Grey Wolf Optimization (GWO) and Recurrent Neural Networks (RNNs) to enhance energy efficiency, reliability, and responsiveness in MIoT systems. The protocol features dynamic clustering, predictive traffic load balancing, and multi-objective optimization for Cluster Head (CH) selection, where RNNs forecast traffic trends and GWO optimizes routing paths. Simulation results demonstrate that the proposed method reduces energy consumption, lowers end-to-end delays, and improves packet delivery ratio (PDR) and network reliability under both static and mobile conditions. Compared to existing methods such as the Krill Herd (KH) algorithm, Dynamic Multi-Sink Routing Protocol (DMS-RP), and Evolutionary Fuzzy Rule-based (EFR) models, the proposed solution exhibits superior performance, validating its scalability and effectiveness for real-world MIoT applications.

Cell-free massive multiple-input–multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing–communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO– ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas, orthogonal time–frequency space (OTFS) modulation, and unmanned aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate–sensing trade-off, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing–communication design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

Unmanned aerial vehicles (UAV)-mounted simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems can provide full-dimensional coverage and flexible deployment opportunities in future 6G-enabled IoT networks. However, practical imperfections such as jittering and airflow of UAV could affect the phase shift of STAR-RIS, and consequently degrade network security. In this respect, this paper investigates the impact of phase shift errors on the secrecy performance of UAV-mounted STAR-RIS-assisted IoT systems. More specifically, we consider a UAV-mounted STAR-RIS-assisted non-orthogonal multiple access (NOMA) system where IoT devices are grouped into two groups: one group on each side of the STAR-RIS. The nodes in each group are considered as potential Malicious nodes for the ones on the other side. By modeling phase estimation errors using a von Mises distribution, an analytical closed-form expressions for the ergodic secrecy rates under imperfect phase adjustment are derived. An optimization problem to maximize the weighted sum secrecy rate (WSSR) by optimizing the UAV placement is formulated and is then solved using a linear grid-based algorithm. Monte Carlo simulations are provided to validate the analytical derivations. The impact of phase estimation errors on system’s secrecy performance is analyzed, providing critical insights for the practical realisation of STAR-RIS deployments for secure UAV-enabled IoT networks.

Unmanned Aerial Vehicles (UAVs) and Intelligent Reflecting Surface (IRS) assisted networks enable high-capacity, reliable beyond fifth generation (B5G) communications in complex urban environments. However, existing systems suffer from inaccurate channel state information (CSI) in non-line-of-sight (NLoS) scenarios, inefficient dynamic spectrum sharing under mobility, and unsustainable energy consumption during UAV operations. Therefore, to address these issues, this work proposes an Orthogonal Matching Markov Chain Monte Carlo Pursuit (OMCMCP) Dynamic Multi-Agent Resource Optimization Framework (DMROF) along with an iterative heuristic algorithm (IHA) for channel estimation, spectrum allocation, and energy-efficient UAV trajectory planning. We formulate an optimization problem for dynamic spectrum allocation in multi-UAV and IRS-assisted networks to maximize the optimal spectrum allocation, power, and trajectory control values. OMCMCP combines sparse recovery and Bayesian inference to acquire NLoS CSI. At the same time, DMROF uses a multi-agent Dueling deep Q network (MADDQN) for real-time spectrum allocation along with quality of service demand-based power allocation (QDPA) to distribute power. The IHA optimizes the trajectory of UAVs by iteratively adjusting the path to minimize energy consumption, considering factors such as flight distance, speed, and the energy required for hovering, maneuvering, and communication. Simulation results show that the proposed approach achieved higher spectral efficiency (16 b/s/Hz), throughput (16 Mb/s), data rate (35 Mb/s), signal-to-information noise ratio (SINR) coverage (95%), and lower energy consumption (500 J) compared to the state-of-the-art. These results validate the capability of the proposed framework to jointly optimize UAV-IRS for spectrum utilization and energy efficiency (EE) in smart city deployments and mission-critical Internet of Things (IoT) applications.

Intelligent reconfigurable surfaces (IRS) have emerged as a promising technology to enhance wireless communications by dynamically controlling the propagation environment. Despite their potential, practical challenges such as effective integration with existing systems and efficient optimization remain critical. This paper investigates the sum capacity enhancement of NOMA-enabled uncrewed aerial vehicle (UAV) communications in vehicular networks assisted IRS. In urban environments where direct links from UAV to vehicles are often obstructed by buildings or other obstacles, the IRS plays a critical role in improving signal quality by reflecting signals toward vehicles. We consider a downlink NOMA transmission scenario, where the UAV serves multiple ground vehicles, and signals are delivered through both direct and IRS-assisted links. A joint optimization problem is formulated to maximize the sum capacity by simultaneously optimizing UAV power allocation and IRS passive beamforming while ensuring a minimum signal-to-interference plus noise ratio requirement for each vehicle. To address the non-convex nature and reduce the complexity of the optimization, we first transform the original problem using the first-order Taylor expansion method. Then, we employ a two-step solution based on the fixed-point iteration method for passive beamforming at the IRS and standard convex optimization for UAV power allocation. The proposed solution is compared with a benchmark scheme with direct UAV-to-vehicle communication without IRS assistance. Numerical results demonstrate that our proposed framework converges quickly and significantly outperforms the benchmarks in terms of system capacity.

Stacked intelligent metasurfaces (SIM) provide a cost-effective and scalable solution for large-scale antenna communications. However, efficient channel state information acquisition and phase shift optimization remain critical challenges. In this paper, we develop a unified framework of low-complexity algorithms for SIM-assisted communication systems to address these issues. Specifically, we propose a generalized two-step codebook construction (TSCC) method that lever-ages two-dimensional angular-domain decoupling to transform planar array beamformer design into two independent one-dimensional linear array beamformer design problems, efficiently solved via the Gerchberg–Saxton algorithm and our proposed majorization–minimization-based proximal-distance (PDMM) algorithm. We further develop a two-stage coded-sliding beam training (TSCSBT) method for low-overhead and high-accuracy beam training, where error-correcting codes are embedded in the first-stage training to enhance robustness against noise, and sliding sampling is subsequently performed around the matched angular samples to improve angular resolution. The proposed framework is further extended to multi-path user channels. Finally, a variable decoupling-based block successive upper bound minimization (VD-BSUM) algorithm is proposed to directly solve the QoS-constrained sum-rate maximization problem through closed-form iterative updates with substantially reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed methods in achieving precise beam pattern realization, improved beam training accuracy and angular resolution, and enhanced sum-rate performance.

This paper presents a novel framework for cognitive radio (CR)-enabled integrated terrestrial and non-terrestrial networks (ITNTNs), comprising a primary low-Earth-orbit (LEO) satellite network and a secondary terrestrial network. In particular, a beyond-diagonal reconfigurable intelligent surface (BD-RIS) mounted secondary base station (BS) reuses the same spectrum to communicate with the secondary user. The proposed framework improves the energy efficiency of the secondary network while ensuring that the interference temperature threshold of the primary LEO network is not violated. The joint optimization of BS power allocation and BD-RIS phase shifts is considered, which results in a highly nonconvex problem. To address this challenge, the Dinkelbach method is first employed to transform the fractional objective function, followed by the development of an alternating optimization strategy. Specifically, the BS power allocation is optimized using the Lagrangian method with Karush-Kuhn-Tucker (KKT) conditions, while the BD-RIS phase shifts are updated through manifold optimization techniques. Numerical results demonstrate that the proposed BD-RIS framework performs better than conventional diagonal RIS (D-RIS) configurations in terms of energy and spectral efficiency, highlighting its potential to enable green, adaptive, and high-capacity 6G ITNTN deployments.

Vehicular Ad Hoc Networks (VANETs) form the foundational communication framework of intelligent transportation systems, facilitating low-latency, vehicle-to-everything data exchange for enhanced traffic efficiency and safety. Accordingly, ensuring secure, efficient, and scalable authentication is essential to maintain communication trustworthiness, especially in highly dynamic and dense traffic scenarios. While traditional public key cryptography (PKC)-based solutions offer strong security guarantees, they are computationally intensive and struggle to scale under VANET workloads. To address these challenges, this paper proposes a novel lightweight handover authentication scheme that integrates pairing-based cryptography with symmetric key primitives to ensure message integrity, anonymity, and unlinkability. The proposed solution is deployed within a real-world Reconfigurable Intelligent Surface (RIS)-assisted communication environment, enhancing the robustness and feasibility of the authentication process during handover. Furthermore, a comprehensive evaluation is conducted, comparing the computational and communication overhead of the proposed scheme with existing cryptographic protocols. Results demonstrate the superior scalability and efficiency of the proposed approach, making it well-suited for next-generation VANET applications.

This work lays the foundations for full-duplex (FD) extremely large (XL) holographic multiple-input multiple-output (MIMO) communication systems to achieve seamless integration of reconfigurable holographic surfaces (RHS) and FD capabilities, enabling ultra-high-capacity, low-latency, and energy-efficient wireless communications. We consider the problem of sum-rate maximization by jointly designing the digital beamformers, holographic beamformer, and holographic combiner at the FD base station to jointly suppress self-interference (SI) and cross-interference. However, this results in a highly non-convex problem, for which a novel alternating optimization combining the minorization-maximization principle and the gradient ascent method is proposed. Simulation results demonstrate that the proposed method almost doubles the spectral efficiency compared to a half-duplex (HD) system.

This work proposes a framework for the robust design of UAV-assisted wireless networks that combine 3D trajectory optimization with user mobility prediction to address dynamic resource allocation challenges. We proposed a sparse second-order prediction model for real-time user tracking coupled with heuristic user clustering to balance service quality and computational complexity. The joint optimization problem is formulated to maximize the minimum rate. It is then decomposed into user association, 3D trajectory design, and resource allocation subproblems, which are solved iteratively via successive convex approximation (SCA). Extensive simulations demonstrate: (1) near-optimal performance with ϵ ≈ 0.67% deviation from upper-bound solutions, (2) 16% higher minimum rates for distant users compared to non-predictive 3D designs, and (3) 10 − 30% faster outage mitigation than time-division benchmarks. The framework’s adaptive speed control enables precise mobile user tracking while maintaining energy efficiency under constrained flight time. Results demonstrate superior robustness in edge-coverage scenarios, making it particularly suitable for 5G/6G networks.

Reconfigurable Intelligent Surface (RIS) technology has emerged as a transformative solution for enhancing satellite networks in next-generation wireless communication. The integration of RIS in satellite networks addresses critical challenges such as limited spectrum resources and high path loss, making it an ideal candidate for next-generation Internet of Things (IoT) networks. This paper provides a new framework based on transmissive beyond diagonal RIS (T-BD-RIS) mounted low earth orbit (LEO) satellite networks with non-orthogonal multiple access (NOMA). The NOMA power allocation at LEO and phase shift design at T-BD-RIS are optimized to maximize the system's spectral efficiency. The optimization problem is formulated as non-convex, which is first transformed using successive convex approximation and then divided into two problems. A closed-form solution is obtained for LEO satellite transmit power using KKT conditions, and a semi-definite relaxation approach is adopted for the T-BD-RIS phase shift design. Numerical results are obtained based on Monte Carlo simulations, which demonstrate the advantages of T-BD-RIS in satellite networks.

This paper introduces a novel strategy for radio resource scheduling in a Coordinated Multi-Point (CoMP) Open Radio Access Network (O-RAN) environment, particularly under the constraints of a non-ideal fronthaul. We propose a comprehensive three-phase scheme, which includes the selection of Cell Edge Users (CEUEs), DPS-based clustering and switching, and a weighted mechanism for Proportional Fair (PF)-based radio resource allocation. Using a modified Vienna 5G system-level simulator, we conducted a series of detailed simulations to validate our approach in a real-world scenario. The simulation results reveal significant enhancements in both the throughput performance of CEUEs and the overall user average throughput. Furthermore, our approach demonstrates substantial improvements in the fairness index, thereby underscoring its potential to significantly improve the efficiency and user experience within 5G networks.

The integration of reconfigurable intelligent surfaces (RIS) into unmanned aerial vehicle (UAV) networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services—spanning logistics to smart surveillance—their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, blackuce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced energy efficiency (EE)-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, quality of service (QoS) guarantees, secrecy rate improvement, latency blackuction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X) communication, and wireless power transfer (WPT). Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

Unmanned aerial vehicles (UAVs) have become essential in advancing multi-access edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RIS) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks.

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this article, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative nonorthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What is more, a warden (Alice) and noncolluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis.

Reconfigurable intelligent surfaces (RISs) are emerging as a transformative technology for sixth-generation (6G) wireless networks. They enable dynamic manipulation of the propagation environment to enhance signal coverage, mitigate interference, and improve spectral and energy efficiencies. However, this flexibility introduces significant security vulnerabilities when RISs are maliciously controlled. This study explores the threats posed by such RISs, focusing on their potential to compromise the security and integrity of 6G networks. From an adversarial perspective, we analyze key attack vectors, including sophisticated jamming attacks that disrupt communication, eavesdropping attacks that intercept communications, and pilot contamination attacks that impair channel estimation accuracy, all contributing to severe performance degradation. For each attack, we detail the underlying mechanisms and adversarial optimization strategies designed to maximize impact. A case study quantifies the practical effects of these malicious RIS-based attacks in a simulated 6G network scenario. This research emphasizes the critical need for robust defense mechanisms and proposes essential research directions to address the evolving threats from malicious RISs, ensuring the security of 6G networks.

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into Passive RIS (PRIS) and Active RIS (ARIS) paradigms. PRIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

Low Earth orbiting (LEO) satellite-assisted wireless communication is increasingly vital for future communication networks due to the significant spectrum scarcity in radio frequency channels, presenting a critical bottleneck. Thus, optimizing the utilization of available radio frequency spectrum has become imperative. Advanced techniques like underlay communication and Rate Split Multiple Access (RSMA) have proven effective in enhancing spectrum utilization. When LEO satellites are applied to tasks such as agricultural assistance, search and rescue operations, and military defense, LEO-to-ground communication can leverage underlay fashion using RSMA to transmit messages to multiple users simultaneously on the same channel. However, conventional underlay communication setups necessitate transmitter cooperation to manage system interference. Enabling non-cooperative systems to communicate in an underlay fashion unlocks the untapped potential of these advanced transmission techniques. This study addresses the challenge of maximizing the RSMA rate of the LEO-to-ground communication system (secondary system) operating in an underlay mode without cooperation with the ground-to-ground communication system (primary system), where the primary network operates in a time-division multiple-access fashion. We propose a dueling-based double deep Q-learning solution to optimize the allowed transmission power at the LEO satellite, ensuring no outage in the primary system. Additionally, we introduce an optimal solution framework to distribute the allowed transmission power among all signals of the secondary devices, maximizing the RSMA rate while meeting the rate requirements of all underlay secondary devices. Simulation results demonstrate that this hybrid solution framework provides excellent performance while ensuring no outage at the primary network.

This manuscript introduces a novel energy-efficient optimization strategy for a zero-energy reconfigurable intelligent reflecting surface (Ze-RIS) supported backscatter communication system employing non-orthogonal multiple access (NOMA). The central objective is to maximize the energy-efficiency of the system by optimizing the several key parameters, including the amplitude reflection coefficient of Ze-RIS, the reflection coefficients of the backscatter tags, transmit beamforming at the base station, and passive beamforming at the Ze-RIS node, while incorporating a practical non-linear energy harvesting model both for the Ze-RIS and backscatter nodes. The proposed algorithm addresses the complex non-convex problem through three stages. Firstly, the transmit beamforming vectors are determined by leveraging the semi-definite programming and successive-convex approximation, while handling the rank-1 constraint with the semi-definite relaxation. Secondly, we determine the amplitude reflection coefficient of Ze-RIS by leveraging the monotonicity property of the objective function. Simultaneously, we compute the reflection coefficients of backscatter tags using the Dinkelbach algorithm, Lagrange duality, and the sub-gradient method. Thirdly, we compute passive beamforming using successive-convex approximation and semi-definite programming techniques, achieving a rank-1 solution through the penalty-based method. Finally, the numerical simulations confirm the effectiveness of the proposed approach, demonstrating its superiority over the benchmark competitors with rapid convergence within a few iterations.

With the rapid evolution toward data-intensive applications and sustainable urban mobility, upcoming sixth-generation (6G) wireless networks must deliver enhanced coverage, high spectral efficiency, and energy optimization across densely populated areas. However, achieving these requirements poses significant challenges due to the dynamic nature of urban environments, high interference in multi-cell systems, and limitations in conventional passive beamforming technologies. To address these challenges, reconfigurable intelligent surface (RIS) is considered a highly promising approach for enabling and improving 6G wireless communications. This is because it has the ability to efficiently manipulate wireless channels at a lower cost. Considerable study has focused on the utilization of conventional diagonal RIS, in which each individual RIS component is linked to its own ground load but not interconnected with other elements. Nevertheless, the uncomplicated structure of classical RIS imposes restrictions on its ability to manipulate passive beamforming. In this study, we consider beyond diagonal RIS (BD-RIS) in the multi-cell transportation system, which goes beyond using diagonal phase shift matrices. In particular, we provide a new optimization framework to maximize the sum rate of BD-RIS assisted multi-cell transportation system by optimizing the power allocation of the base station and phase shift design of BD-RIS in each cell. We employ the block coordinate descent method to transform the original optimization problem and achieve a local optimal based on standard convex approaches. Numerical results demonstrate the benefits of adopting BD-RIS in multi-cell transportation systems compared to the classical RIS architecture.

The terahertz communications have the potential to revolutionize data transfer with unmatched speed and facilitate the development of new high-bandwidth applications. This paper studies the performance of downlink terahertz system assisted by beyond diagonal intelligent reconfigurable surface (BD-IRS). For enhanced energy efficiency and low cost, a joint precoding and BD-IRS phase shift design satisfying the 1-bit resolution constraints to maximize the spectral efficiency is presented. The original problem is non-linear, NP-hard, and intricately coupled, and obtaining an optimal solution is challenging. To reduce the complexity, we first transform the optimization problem into two problems and then iteratively solve them to achieve an efficient solution. Numerical results demonstrate that the proposed approach for the BD-IRS assisted terahertz system significantly enhances the spectral efficiency compared to the conventional diagonal IRS assisted system.

Reflective Intelligent Surface (RIS)-assisted Ultra-Massive MIMO (Um-MIMO) systems in the terahertz (THz) spectrum are gaining attention for surpassing current wireless system limitations. However, limited diffraction at these frequen-cies typically results in low-rank Um-MIMO channels, completely reducing the potential for spatial multiplexing gains. In this work, we aim at promoting a new research direction towards the strategies for achieving high-rank Um-MIMO for RIS-assisted THz communications. The maximum achievable spatial multiplexing gain over the RIS-assisted channel is analyzed, and the optimal antennas and RIS elements placement strategy is proposed for achieving extremely high-rank Um-MIMO channels. Simulation results demonstrate that while conventional Um-MIMO THz systems display low-rank, the proposed approach enables achieving a rank on the order of hundreds for the Um-MIMO THz systems.

Developing wireless communication technologies is an ongoing process to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. Using non-orthogonal multiple access (NOMA) and backscatter communication (BC) has surfaced as an advantageous approach for enhancing energy efficiency (EE), maximizing sum rates, ensuring security, and optimizing resource allocation. NOMA permits multiple users to share time and frequency resources even without the requirement of antenna arrays, whereas BC employs ambient RF signals for low-power communication. By integrating the advantages of NOMA and BC, NOMA-based BC provides a solution for future energy-efficient and low-power networks. Despite its potential, there is a lack of a comprehensive overview of NOMA-BC, necessitating a systematic survey that covers its principles, applications, challenges, and future directions. This survey aims to bridge the gap by exploring NOMA-BC within B5G and 6G networks. We delve into its technical aspects, performance optimization techniques, and real-world applications to enhance understanding and knowledge. First, we cover topics such as enhancing EE, maximizing the sum rates, ensuring security, and analyzing performance. Our primary goal is to provide researchers and practitioners with valuable insights that enable them to grasp the capabilities and benefits of NOMA-BC. To achieve this, we comprehensively analyze the performance of various schemes by presenting detailed summary tables. These analyses cover a range of scenarios, methods, and objectives, focusing on emerging B5G technologies such as reconfigurable intelligent surfaces (RIS), visible light communication (VLC), and unmanned aerial vehicle (UAV) communication. By examining NOMA-BC’s effectiveness within these contexts, we aim to provide a holistic view of its potential and applicability in diverse technological domains. Moreover, our survey identifies and discusses open research challenges and proposes future directions to guide researchers toward unexplored areas and facilitate advancements in NOMA-BC.

In this manuscript, we introduce an efficient resource allocation strategy to enhance the security of an intelligent omni-surface (IOS) assisted secure Internet-of-things (IoT) enabled non-orthogonal multiple access (NOMA) network under residual hardware impairments (RHIs) resulting from imperfect hardware design. In particular, the goal is to maximize the sum secrecy rate of the considered multi-cluster based secure NOMA system assisted by an IOS node. This is achieved by optimizing both the active beamforming vectors of NOMA users within the transmission and reflection regions of the system, and the transmission and reflection coefficients of the IOS node, while adhering to quality-of-service, successive interference cancellation, power budget, and energy conservation constraints. Moreover, the presented alternating optimization framework tackles the significantly non-convex optimization problem through a two-stage process. Firstly, the active beamforming vectors are obtained using successive convex approximation (SCA) and second-order conic programming (SOCP) techniques. Secondly, based on the determined active beamforming vectors, the transmission and reflection coefficients of the IOS node are computed utilizing SCA and semi-definite relaxation (SDR) techniques, where rank-1 solution is achieved through Gaussian randomization method. Ultimately, the numerical simulations validate the efficacy of the suggested method over competing benchmarks, in terms of sum secrecy rate, showcasing its superiority in achieving fast convergence within a limited number of iterations.

In the constantly developing realm of the Internet of Things (IoT), guaranteeing fast data transfer and a smooth user experience is critical. In IoT contexts with limited resources, congestion control is crucial for sustaining network performance. This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model’s predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods. The findings illustrate the concrete advantages of LSTM-enhanced congestion control in IoT, highlighting its potential to reduce network congestion and improve the overall user experience.

Reconfigurable Intelligent surfaces (RIS) have the potential to significantly improve the performance of future 6G LEO satellite networks. In particular, RIS can improve the signal quality of ground terminal, reduce power consumption of satellite and increase spectral efficiency of overall network. This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.

The recent development of metasurfaces, which may enable several use cases by modifying the propagation environment, is anticipated to substantially affect the performance of sixth-generation (6G) wireless communications. Metasurface elements can produce passive subwavelength scattering to enable a smart radio environment. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which refers to reconfigurable intelligent surfaces (RISs) that can transmit and reflect concurrently (STAR), is gaining popularity. In contrast to the widely studied RIS, which can only reflect the wireless signal and serve users on the same side as the transmitter, the STAR-RIS can reflect and refract (transmit), enabling 360° wireless coverage, thus serving users on both sides of the transmitter. This article presents a comprehensive review of the STAR-RIS, focusing on the most recent schemes for diverse use cases in 6G networks, resource allocation, and performance evaluation. We begin by laying the foundation for RIS (passive, active, and STAR-RIS), and then discuss the STAR-RIS protocols, advantages, and applications. In addition, we categorize the approaches within the domain of use scenarios, which include increasing coverage, enhancing physical-layer security (PLS), maximizing sum rate, improving energy efficiency (EE), and reducing interference. Next, we will discuss the various strategies for resource allocation and measures for performance evaluation. We aimed to elaborate, compare, and evaluate the literature regarding setup, channel characteristics, methodology, and objectives. In conclusion, we examine this field’s open research problems and potential future prospects.

In this manuscript, we present an energy-efficient alternating optimization framework based on the multiantenna ambient backscatter communication (AmBSC)-assisted cooperative nonorthogonal multiple access (NOMA) for next-generation (NG) Internet of Things (IoT)-enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multicluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PACs) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multiantenna-assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in intercluster interference (ICI) (among different clusters) and intracluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF)-based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intracluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the Quality-of-Service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered nonconvex passive-beamforming problem is transformed into a standard semidefinite programming (SDP) problem by exploiting the successive-convex approximation (SCA), as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of backscatter communication (BC) and artificial intelligence (AI). BC is an energy-saving and cost-effective communication method where passive backscatter devices (BDs) communicate by modulating ambient radio-frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This article brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas, such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with reconfigurable intelligent surfaces (RISs), UAV, and vehicular networks. Finally, in the discussion section, we summarize the solutions, provide lessons learned and challenges, and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.

The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput.

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano’s formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.

Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained significant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reflect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state information (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reflection coefficient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we first decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efficient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the benefits of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efficiency.

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Low Earth orbit (LEO) satellite communication has drawn particular attention recently due to its high data rate services and low round-trip latency. It is low-cost to launch and can provide global coverage. However, the spectrum scarcity might be one of the critical challenges in the growth of LEO satellites, impacting severe restrictions on the development of ground-space integrated networks. To address this issue, we propose rate splitting multiple access (RSMA) for cognitive radio (CR) enabled nongeostationary orbit (GEO)-LEO coexisting satellite network. In particular, this work aims to maximize the system's sum rate by simultaneously optimizing the power allocation and sub carrier beam assignment of LEO satellite communication while restricting the interference temperature to GEO satellite users. The problem of sum rate maximization is formulated as non-convex and a Global optimal solution is challenging to obtain. Therefore, we first employ the successive convex approximation technique to reduce the complexity and make the problem more tractable. Then for the power allocation, we exploit Karush-Kuhn-Tucker (KKT) condition and adopt an efficient algorithm based on the greedy approach for subcarrier beam assignment. We also propose two suboptimal schemes with fixed power allocation and random sub carrier beam assignment as the benchmark. Results demonstrate the benefits of the proposed scheme compared to the benchmark schemes.

Reconfigurable intelligent surface (RIS), non-orthogonal multiple access (NOMA), and underwater optical wireless communication (UOWC) are paradigms of technologies that drive the development of next generation communication systems. In this paper, we investigate the performance of a NOMA-based RIS-assisted hybrid radio frequency (RF)-UOWC system. The ship works as a relay that redirects the received signal to two underwater destinations simultaneously. Due to the interruption of the direct link between the base station and the ship floating on the surface of the water, communication will be carried out via an RIS fixed to an intermediate building. In this paper, we provide new analytical expressions for the outage probability (OP), asymptotic analyses of the OP, and diversity order (D) to gain insights into the system performance. The results showed that the diversity order depends on the UOWC receiver detection technique. In the end, we illustrated that the NOMA-based RIS-assisted system significantly improves the outage performance of hybrid RF-UWOC systems over a benchmark system.

In the last decade, a sharp surge in the number of user proximity wireless devices (UPWDs) has been observed. This has increased the level of electromagnetic field (EMF) exposure of the users substantially and hence, the possible physiological effects. Ambient backscatter communications (ABC) has appeared to be a promising solution to reduce the power consumption of UPWDs by converting ambient radio frequency (RF) signals into useful signals while non-orthogonal multiple access (NOMA) is a compelling multiplexing scheme for enhanced spectral efficiency. This paper utilises a novel combination of ABC and NOMA to reduce the EMF in the uplink of wireless communication systems. This contemporary approach of EMF-aware resource optimization is based on k-medoids and Silhouette analysis. To curtail the uplink EMF, a power allocation strategy is also derived by converting a non-convex problem to a convex one and solving accordingly. The numerical results exhibit that the proposed ABC, NOMA, and unsupervised learning based scheme achieves a reduction in the EMF by at least 75% in comparison to the existing solutions.

Backscatter communications (BC) has emerged as a promising technology for providing low-powered transmissions in nextG (i.e., beyond 5G) wireless networks. The fundamental idea of BC is the possibility of communications among wireless devices by using the existing ambient radio frequency signals. Non-orthogonal multiple access (NOMA) has recently attracted significant attention due to its high spectral efficiency and massive connectivity. This paper proposes a new optimization framework to minimize total transmit power of BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the quality of services. More specifically, the base station (BS) transmits a superimposed signal to its associated roadside units (RSUs) in the first time slot. Then the RSUs transmit the superimposed signal to their serving vehicles in the second time slot exploiting decode and forward protocol. A backscatter device (BD) in the coverage area of RSU also receives the superimposed signal and reflect it towards vehicles by modulating own information. Thus, the objective is to simultaneously optimize the transmit power of BS and RSUs along with reflection coefficient of BDs under perfect and imperfect channel state information. The problem of energy efficiency is formulated as non-convex and coupled on multiple optimization variables which makes it very complex and hard to solve. Therefore, we first transform and decouple the original problem into two sub-problems and then employ iterative sub-gradient method to obtain an efficient solution. Simulation results demonstrate that the proposed BC-NOMA V2XneT provides high energy efficiency than the conventional NOMA V2XneT without BC.

Reflecting intelligent surfaces (RIS) is a low-cost and energy-efficient solution to achieve high spectral efficiency in sixth-generation (6G) networks. The basic idea of RIS is to smartly reconfigure the signal propagation by using passive reflecting elements. On the other side, the demand of high throughput geostationary (GEO) satellite communications (SatCom) is rapidly growing to deliver broadband services in inaccessible/insufficient covered areas of terrestrial networks. This paper proposes a GEO SatCom network, where a satellite transmits the signal to a ground mobile terminal using multicarrier communications. To enhance the effective gain, the signal delivery from satellite to the ground mobile terminal is also assisted by RIS which smartly shift the phase of the signal towards ground terminal. We consider that RIS is mounted on a high building and equipped With multiple re-configurable passive elements along with smart controller. We jointly optimize the power allocation and phase shift design to maximize the channel capacity of the system. The joint optimization problem is formulated as nonconvex due to coupled variables which is hard to solve through traditional convex optimization methods. Thus, we propose a new $\epsilon-$ optimal algorithm which is based on Mesh Adaptive Direct Search to obtain an efficient solution. Simulation results unveil the benefits of RIS-assisted SatCom in terms of system channel capacity.

The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide a safer, stress-free, and more enjoyable driving experience. Moreover, emerging applications and services necessitate massive volumes of data, real-time data processing, and ultrareliable and low-latency communication (URLLC). However, the computing capability of current intelligent vehicles is minimal, making it challenging to meet the delay-sensitive and computation-intensive demand of such applications. In this situation, vehicular task/computation offloading toward the edge cloud (EC) and vehicular cloudlet (VC) seems to be a promising solution to improve the network’s performance and applications’ Quality of Service (QoS). At the same time, artificial intelligence (AI) has dramatically changed people’s lives. Especially for vehicular task offloading applications, AI achieves state-of-the-art performance in various vehicular environments. Motivated by the outstanding performance of integrating reinforcement learning (RL)/deep RL (DRL) to the vehicular task offloading systems, we present a survey on various RL/DRL techniques applied to vehicular task offloading. Precisely, we classify the vehicular task offloading works into two main categories: 1) RL/ DRL solutions leveraging EC and 2) RL/DRL solutions using VC computing. Moreover, the EC section-based RL/DRL solutions are further subcategorized into multiaccess edge computing (MEC) server, nearby vehicles, and hybrid MEC (HMEC). To the best of our knowledge, we are the first to cover RL/DRL-based vehicular task offloading. Also, we provide lessons learned and open research challenges in this field and discuss the possible trend for future research.

Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this article, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the near-optimal solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework.

peer reviewed

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

Non-orthogonal multiple access (NOMA) has been investigated to support massive connectivity for Internet-of-things (IoT) networks. However, since most IoT devices suffer from limited power and decoding capabilities, it is not desirable to pair a large number of devices simultaneously, which encourages two-user NOMA grouping. Additionally, most existing techniques have not considered the diversity in the target QoS of IoT devices, which may lead to spectrum inefficiency. Few investigations have partially considered that issue by using an order-based power allocation (OPA) approach, where the power is allocated according to the order to the user’s target throughput within a priority-based NOMA (PNOMA) group. However, this does not fully capture the effects of diversity in the values of the users’ target throughputs. In this work, we handle both problems by considering a throughput-based power allocation (TPA) approach, that captures the QoS diversity, within a three-users PNOMA group as a compromise between spectral efficiency and complexity. Specifically, we investigate the performance of a time-division PNOMA (TD-PNOMA) scheme, where the transmission time is divided into two-time slots with two-users per PNOMA group. The performance of such TD-PNOMA is compared with a fully PNOMA (F-PNOMA) scheme, where the three users share the whole transmission time, in terms of the ergodic capacity under imperfect successive interference cancellation (SIC). The results reveal the superiority of TPA compared with OPA approach in both schemes, besides that the throughput of both schemes can outperform each other under imperfect SIC based on the transmit signal-to-noise ratio and the deployment scenarios.

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.

The brain tumor is one of the primary diseases that endanger human life and health. Multi-modality magnetic resonance imaging (MRI) for tumor analysis is one of the key techniques for clinical diagnosis. Experienced experts artificially segment the classical method of brain tumor segmentation according to their anatomical and pathological knowledge, which is time-consuming. In this paper, we propose a novel deep neural network structure, namely Feature Mining Networks (FMNet), for brain tumor segmentation. The proposed FMNet adopts three innovative structure, including semantic information mining unit (SIMU), macro information mining unit (MIMU), and a feature correction unit (FCU). These three units can enhance the mining of semantic information and spatial information, and further, modify the information in a direction that is conducive to segmentation results. Each unit can bring significant improvement in segmentation performance. We evaluate the proposed framework on BraTS2017 and BraTS2018 dataset. The experimental results show that our FMNet performs better than state-of-the-art networks such as fully convolutional networks (FCN), U-Net, VGG, and Hybrid Pyramid U-Net (HPUNet).

Digital technologies are rapidly shaping the modern concepts of urbanization. It is a key element of developing practical smart cities of the future. In fact, they are the catalyst for the increasing networking of all areas of life in a smart city. Recent development in the domain of communication technologies has opened new avenues to realize the concept of smart cities. One of such communication technology is non-orthogonal multiple access (NOMA) for future cellular communications. This article, therefore, focuses on the interference management of uplink cellular NOMA systems. Specifically, we propose a power optimization technique for NOMA to improve the sum-rate in a multi-cell environment. We also consider Nakagami-m faded links to analyze the applicability of our proposed scheme under various channel conditions. The simulation results show that the proposed NOMA approach outperforms conventional orthogonal multiple access (OMA) technique in the multi-cell uplink scenario.

Recently, non‐orthogonal multiple access (NOMA) has been considered as a promising radio access technique for the upcoming fifth generation networks. In this study, the authors consider the downlink multi‐cell multi‐user cellular network, where the base station (BS) located in the centre of each cell intends to communicate with multiple random distributed user equipments (UEs) using NOMA protocol. They formulate an optimisation problem to increase the sum network capacity under various practical constraints. Especially, the transmission power budgets for BSs, power allocation for UEs, and minimum rate requirements per UE are optimised. Moreover, they obtain the optimal power allocation via local optimal solution iteratively, which satisfies Karush–Kuhn–Tucker conditions. The simulation results show that the proposed scheme can converge after few iterations and achieve higher sum capacity compared with the non‐optimal NOMA and orthogonal multiple access schemes.

The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multiuser underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.