Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Achieving these gains can pose a significant challenge for MA-enabled ISAC systems operating in the near-field due to the corresponding spherical wave propagation. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimal sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose an algorithm that optimizes the sensing receive combiner, the communication precoding matrices, the sensing transmit beamformer and the positions of the users' MAs in an alternating manner. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems with only fixed antennas. Additionally, we demonstrate that the highest WSR is obtained when larger weights are allocated to the users placed closer to the BS, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.

As electric vehicles (EVs) join the 6G Internet of Vehicles (6G-IoV), they must charge while moving and process data in real time. Static plug-in charging and on-board CPUs cannot meet these dual demands. We therefore combine two technologies. First, wireless power transfer (WPT) on electrified roads (eROAD) keeps an EV’s battery topped up throughout its trip. Second, a privacy-preserved multi-agent federated deep-RL (MA-FDRL) framework decides, every slot, where each task runs—on the vehicle, a roadside unit (RSU), or the base station (BS). Federated learning keeps raw data local; differential privacy protects model updates; deep reinforcement learning allocates radio, compute, and energy resources for many agents in real time. Experiments show the scheme cuts task-offloading latency, balances edge-server load, and scales gracefully with network size. The outcome suggests an energy-conscious architecture capable of meeting the low-latency and privacy needs of 6G-IoV.

NOMA-aided cognitive satellite aerial terrestrial integrated networks (CSATINs) are considered revolutionary and key technologies for 6G Internet of Things (6G-IoT), offering enhanced connectivity, high spectral efficiency, and broad coverage. In this article, we first establish trustworthy CSATINs with multiple aerial relays, aiming to achieve reliable communication in the presence of an eavesdropper. Then, to enhance the system’s covert performance, we propose an unmanned aerial vehicle scheduling scheme. Moreover, based on the established covert system model, we derive the closed-form expressions of detection error probability (DEP), covert outage probability (COP), and effective covert rate (ECR). Particularly, an optimization is proposed to enhance the covert performance of the considered system. Finally, Monte Carlo simulations are given to validate the correctness of the theoretical analysis, demonstrating that the reliability and covertness of the proposed system can be simultaneously enhanced by appropriately adjusting the power allocation coefficients, jamming power, and the transmission power of the satellite and UAVs.

This paper investigates a drone (aka UAV)-assisted physical layer security framework for space–air–ground integrated networks (SAGINs) in the presence of multiple eavesdroppers. A single full-duplex UAV is deployed to support satellite-to-ground communications by simultaneously relaying desired signals to legitimate users and transmitting artificial noise to degrade the reception quality of eavesdroppers. To enhance secure connectivity, we formulate a max–min secrecy rate optimization problem that jointly considers sub-channel allocation and power distribution. The sub-channel allocation is optimized using a constrained genetic algorithm, which efficiently handles the combinatorial nature of the problem. Additionally, power allocation is optimized through a nested-loop approach, in which the outer loop employs Bayesian optimization to address complex objective functions, while the inner loop makes the allocation tractable using variable substitutions and approximation methods to overcome non-convexity. The simulation results demonstrate that the proposed method outperforms the benchmark schemes in terms of secrecy performance, particularly under stringent resource and security constraints in SAGINs.

The rapid growth of connected vehicles in sixth-generation (6G) networks demands real-time, privacy-preserving offloading of ultra-reliable, low-latency communications (URLLC) tasks. We propose Quantum-federated deep reinforcement learning (Q-FDRL). This decentralized federated learning framework tightly integrates a large artificial intelligence (AI) model (LAM), composed of a multi-layer transformer encoder with a shallow variational quantum circuit (VQC) head. This hybrid architecture enables compact, high-dimensional state representation under on-device compute constraints. Vehicular task offloading is modeled as a multi-agent Markov game-including full data partitioning, power-splitting uplink, finite-blocklength transmission, and dynamic CPU allocation- and solved via local advantage actor-critic (A2C) updates at vehicles, unmanned aerial vehicles, and roadside units. To guarantee rigorous (ε, δ)-differential privacy, we apply Gaussian-mechanism gradient clipping and secure one-time-pad encryption over quantum key distribution (QKD) links, with a moments accountant bounding the cumulative privacy loss to ε ≤ 5. In simulations over 250 episodes, Q-FDRL converges approximately ∼ 27.27% faster and reduces early-stage variance by ∼ [11.11, 42.86]% compared to centralized A2C and classical federated baselines, demonstrating its effectiveness for scalable, privacy-preserving vehicular resource management.

In this paper, we investigate the performance of a non-orthogonal multiple access (NOMA)-enhanced active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system under a non-terrestrial network (NTN) framework. The performance is analyzed in terms of outage probability, ergodic capacity, beampattern gain, and probability of detection (PoD). To this end, the end-to-end equivalent channel distribution under maximum ratio transmission beamforming is derived and modeled as a Gamma distribution. Based on this, a closed-form expression for the outage probability (OP) is obtained in terms of Meijer G-functions. The accuracy of the analytical results is validated through Monte Carlo simulations. To provide further insight, we derive the asymptotic expression of the OP and the corresponding diversity order. Additionally, an approximate expression for the ergodic capacity is derived using the method of moments, along with its upper and lower bounds. To evaluate the impact of the active RIS in mitigating the impact of multiplicative fading through amplification of the incident signal, its performance is compared against a baseline passive RIS scheme, revealing significant performance improvements. In particular, the outage probability is improved by approximately 72.94% and 73.33% for the weak user (D1) and strong user (D2), respectively, when employing an active RIS compared to a passive RIS at a transmit SNR of 22 dBm. The influence of practical impairments, including hardware impairments, imperfect channel state information, and imperfect successive interference cancellation, is also examined. The results show that increasing the number of RIS elements and the number of antennas at the satellite can effectively alleviate the adverse effects of these impairments. On the sensing side, the proposed ISAC system is compared with a conventional radar system in terms of the PoD for aerial targets located at various positions, and it additionally demonstrates beampattern gain. The results are obtained under the assumptions of ideal RIS phase configuration and line-of-sight (LoS) between the satellite and targets, representing key limitations of the analysis.

Integrating large-scale antenna arrays is essential for overcoming capacity limitations in wireless communications. In this work, we examine a novel sixth-generation (6G) secure simultaneous wireless information and power transfer (SWIPT) system, where a transmitter equipped with an extremely large-scale antenna array (ELAA) operates in the near-field region. In our design, the transmitter concurrently delivers confidential data to information receivers and energy to zero-energy devices (ZEDs) via non-orthogonal multiple access (NOMA). A key innovation of our approach is the specialized near-field beamfocusing technique derived from a three-dimensional spherical channel model, which explicitly accounts for the unique propagation characteristics of near-field communications and distinguishes our method from traditional far-field designs. We formulate a non-convex optimization problem aimed at maximizing the secrecy rate while satisfying minimum quality-of-service and energy harvesting requirements. To solve this problem, we develop an iterative algorithm based on weighted sum-rate maximization and sequential convex approximations that effectively mitigate interference and enhance beamfocusing performance. Numerical simulations demonstrate that, with a 64-element uniform linear array and 40 dBm transmit power, our near-field NOMA system achieves an 18.41% higher secrecy rate than near-field spatial division multiple access (SDMA) and a 36.78-fold improvement over near-field orthogonal multiple access (OMA), along with a 6.39 dBm increase in harvested power relative to SDMA. These results underscore the critical role of specialized near-field design in next-generation 6G networks and its significant implications for industrial internet-of-things (IoT) and Industry 4.0 applications.

With the arrival of sixth-generation communication systems, advanced technologies like reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and non-orthogonal multiple access (NOMA) are poised to drive a broad range of Internet of Things (IoT) applications. Integrating ISAC into multi-input single-output (MISO) networks calls for a reassessment of performance metrics such as outage probability and sensing rate. Furthermore, a critical challenge is managing heterogeneous user deployments and dynamically adapting to varying user locations while minimizing interference. To address these challenges, this work proposes an innovative multi-sector RIS framework. By dividing the RIS into independently controlled sectors, the system dynamically selects the sector closest to each user. For downlink transmission, a dual-function base station (BS) utilizes NOMA to serve the user clusters and transmit a sensing signal. The RIS dynamically selects the sector closest to the close-proximity user based on their location. To support this, the proposed approach employs a nearest-sector selection strategy centered around a reference close-proximity user. Closed-form approximations for the outage probability are then derived, assuming a blocked direct link between the BS and the users. This framework also enables ISAC by transmitting sensing signals within the selected sector, facilitating both user communication and target detection. We characterize the sensing by the sensing rate, with results showing that increasing RIS elements in the multi-sector design enhances the sensing rate. Overall, the proposed system demonstrates superior performance over traditional simultaneously transmitting and reflecting (STAR)RIS configurations and space division multiple access (SDMA) systems. In particular, the proposed system achieves a gain of 8dB, 12dB and 2dB over SDMA, conventional RIS and STAR-RIS systems, respectively.

Driven by the growing demands for reliability and high data rates, uplink rate-splitting multiple access (RSMA) has emerged as a promising technique for next-generation wireless networks. However, in practical scenarios, particularly under imperfect SIC (ipSIC), the presence of residual interference (RI) significantly degrades performance, especially at high transmit power levels. This RI stems from two key sources: intra-user split packet interference (SPI) due to imperfect cancellation of a user’s own split streams, and inter-user residual packet interference (IPI) from imperfect cancellation of other users’ signals. As these impairments accumulate across multiple SIC stages, the inherent benefits of rate splitting may progressively diminish, potentially resulting in performance degradation compared to conventional schemes such as non-orthogonal multiple access (NOMA). To address this challenge, we propose a split-packet interference management (SPIM) framework that enhances uplink RSMA performance under ipSIC by structurally enforcing orthogonality between a user’s own split streams. This design reduces the reliance on successive decoding and effectively limits the propagation of RI, especially from SPI, while preserving the benefits of rate-splitting. We consider a two-user system and perform a comprehensive outage probability analysis under both perfect SIC (pSIC) and ipSIC conditions, leveraging a copula-based statistical modelling framework to accurately model the dependency between streams. Additionally, we derive analytical expressions for the ergodic sum rate thus, providing insights into the long-term performance. While the core analysis focuses on a two-user setup, the SPIM RSMA architecture is extended to larger user groups with a scalable receiver design. Simulation results closely match analytical findings and demonstrate that while SPIM RSMA performs comparably to conventional RSMA under pSIC, it significantly outperforms both conventional RSMA and NOMA under ipSIC conditions in terms of outage probability, sum-rate, and sum-throughput. These results underscore the structural versatility of SPIM RSMA, which proactively mitigates SPI, thereby preserving the advantages of rate splitting in SIC-constrained environments.

We propose a novel integrated sensing and communication (ISAC) framework that combines active reconfigurable intelligent surfaces (RIS) with a movable antenna (MA) array at the base station to jointly enhance the communication throughput and the radar sensing resolution. Unlike conventional architectures that employ passive RIS or fixed antenna arrays, the proposed framework leverages dual-domain reconfigurability, electromagnetic and geometric, by jointly optimizing transmit beamforming, RIS reflection coefficients with amplification constraints, and the spatial positions of the mobile antennas. The system is modeled under a practical RIS noise amplification model and subject to the constraints of stringent signal-to-interference-plus-noise ratio (SINR), radar beampattern, and transmission power. A unified optimization problem is formulated and decomposed into tractable subproblems using an alternating optimization approach based on semidefinite relaxation (SDR), successive convex approximation (SCA), and convex programming. Numerical results confirm that the proposed design significantly outperforms conventional passive RIS and fixed array systems in terms of both radar and communication metrics, particularly under dynamic channel conditions and constrained power budgets.

The novel flexible-antenna technology, known as pinching antennas, has recently attracted significant research interest. By embedding discrete dielectric materials, a pinching antenna can be dynamically activated at arbitrary points along a dielectric waveguide, enabling flexible control of large-scale path loss and on-demand coverage extension. This paper investigates the performance analysis of a downlink rate-splitting multiple access (RSMA) enhanced pinching-antenna assisted integrated sensing and communication (ISAC) system. Specifically, we consider a sensing-centric scenario in which a single pinching antenna simultaneously serves two communication users and performs target sensing, where the sensing target and one user are co-located in the same room and are served via a line-of-sight (LoS) link, while the other user resides in a separate room and is connected through a non-LoS (NLoS) link. We derive novel closed-form expressions for the outage probabilities (OPs) of both communication and sensing tasks. The communication OP is obtained through an information-theoretic approach, whereas the sensing OP is evaluated using a mean square error (MSE) based approach. In addition, the system throughput for the communication task and average sensing MSE for the sensing task are also evaluated. To gain further insights, asymptotic OP expressions are also derived for both communication and sensing tasks in the high signal-to-noise ratio (SNR) regime. Monte Carlo simulations are performed to validate the analytical results. Numerical results demonstrate that the proposed pinching-antenna-assisted RSMA–ISAC system outperforms pinching-antenna-assisted non-orthogonal multiple access (NOMA)–ISAC and space-division multiple access (SDMA)–ISAC in terms of communication reliability and sensing accuracy. Furthermore, compared with a conventional antenna-assisted RSMA–ISAC benchmark, the proposed framework achieves significant sensing performance gains due to reduced effective sensing distance and near-field spatial focusing enabled by pinching antennas, while exhibiting a controllable sensing–communication trade-off governed by power allocation and antenna positioning. Finally, we analyze the impact of pinching-antenna height, room size, and other key system parameters on the performance of both communication and sensing tasks.

Pinching-antenna systems (PASS) route energy through low-loss dielectric waveguides and radiate via reconfigurable pinching antennas (PAs), enabling large, shapeable apertures with minimal radio chains. We study a near-field multicast downlink network that extends single-waveguide PASS to a coordinated multi-waveguide array and jointly optimizes PA positions and beams. We first develop a cascaded channel that couples in-waveguide and free-space propagation, and pose a worst-case multicast objective under spacing, coupling span, and power constraints. A two-stage co-design then follows. Stage I performs layout planning as a constrained bi-objective placement that maximizes the worst-user signal-to-noise ratio (SNR) while minimizing a wrapped-phase residual; when solved with the non-dominated sorting genetic algorithm (NSGA) II, it yields feasible Pareto layouts. Stage II fixes a knee layout obtained from Stage I and refines the multicast beam via a convex semi-definite relaxation (SDR)-successive convex approximation (SCA) formulation with a feasibility warm start, thereby recovering rank-one beams. Numerical results reveal that over wide ranges of transmit power, coupling span, PA per waveguide, number of waveguides, user count, user range, and base-station height, the proposed design outperforms a single-waveguide PASS and$\boldsymbol {x}$or$\boldsymbol {y}$-aligned uniform linear arrays, delivering higher worst-user rates as well as sharply lowering the per-user rate variance. The study also identifies broad coupling-length ranges where gains saturate and shows that a moderate number of pinches and additional waveguides help improve spatial coverage until a geometry-limited plateau is reached. These effects arise from in-waveguide proximity and lateral phase control, positioning multi-waveguide PASS as a practical, flexible antenna option for next-generation communication.

With great potential in providing global coverage and real-time service, recently, direct satellite-to-device (DS2D) communication has attracted considerable attention. However, how to effectively utilize the costly satellite resources to satisfy the on-demand massive connectivity remains a huge challenge. This paper proposes a cost-effective hetero-granular resource allocation framework that combines the advantages of ground-based scheduling and spaceborne scheduling. In specific, we aim to optimize both the cell-level and user-level scheduling in a beam-hopping system. The formulated optimization problem is first decomposed into a coarse-grained scheduling problem among cells using historical demand information on the ground station, and a fine-grained scheduling problem among users using real-time service demands on satellite. We solve the mixed-integer non-linear programming problem of coarse-grained scheduling with cross-entropy and quantum particle swarm optimization algorithms to find the global optimum, exploiting the adequate ground-based computational resources. The fine-grained scheduling problem is solved with a generalized-benders-decomposition-based algorithm to accommodate the limited spaceborne resources, which decouples power and bandwidth allocation based on a closed-form solution of optimal dual variables in the primal power allocation problem. Simulation results demonstrate that the proposed method effectively reduces the length of the waiting queue by up to 25.05% compared to the existing methods.

We study joint phase-amplitude control for near-field holographic multiple-input multiple-output systems in intelligent autonomous transport systems. Under exact Fresnel propagation and per-user quality-of-service (QoS) constraints, the design couples discrete tile phases, continuous amplitudes, a multi-user digital precoder, and non-orthogonal multiple access (NOMA) power fractions, resulting in a mixed-integer nonconvex program. We propose a hybrid quantum-classical solver, the Quantum approximate-and-gradient optimizer for the near field (QAG-NF), which alternates between a blockwise quantum approximate search over tile phases and exact-gradient updates of amplitudes, power shares, and the precoder. A penalized objective, monotone-accept rule, and Armijo backtracking procedure preserve feasibility and guarantee non-decreasing utility at accepted iterates. The continuous stage uses geometry-consistent preconditioners, including Jacobian-scaled amplitude steps and a softmax Fisher/natural gradient for NOMA powers, while the discrete stage uses few-qubit shallow circuits per block, making the method compatible with near-term noisy quantum hardware and practical simulators. Beyond solver design, we incorporate Doppler-averaged effective gains to impose conservative power floors consistent with successive interference cancellation, thereby safeguarding QoS throughout the line search. In a representative deployment, QAG-NF provides a substantial performance gain at equal base-station power, improving the sum rate by about 31.64% over a near-field projected-gradient baseline and by roughly$7.39\times $over a far-field surrogate, while also achieving higher Jain fairness.

This work addresses the problem of joint robust transmission, reflection, and reception strategy design in an active reconfigurable intelligent surface (ARIS)-assisted multiuser multiple-input multiple-output (MIMO) system. Specifically, a signal-to-interference-noise (SINR) maximization problem has been formulated by jointly optimizing the transmit beamforming matrix at the base station (BS), the linear reception filters at the users, and the reflection coefficient matrix at the ARIS. The optimization has been performed under constraints on the BS transmit power, the maximum amplification power of the ARIS, and the maximum amplitude coefficients of the ARIS. To jointly optimize RIS-assisted systems, this paper proposes an efficient deep learning (DL) model. Specifically, a multi-layer perceptron (MLP)-based deep neural network (DNN) has been designed to effectively approximate the optimal solution. Further, to handle channel state information (CSI) uncertainties arising from estimation errors and environmental variations, a novel uncertainty injection scheme has been proposed for training DL models. The output of the solution is perturbed through uncertainty injection. The model learns a robust beamforming matrix, linear reception filters, and reflection configurations that maintain high SINR under worst-case channel conditions. Simulation results demonstrate that, for the optimized phase and ARIS configuration, the proposed DL trained with the UI scheme achieves a 26.23% SINR improvement compared to the model trained without UI (WUI). In addition to the ARIS, the performance of the passive reconfigurable intelligent surface has also been analyzed. Further, the time complexity and robustness of the proposed model have been evaluated.

In this article, we investigate the performance of a holographic active reconfigurable intelligent surface (HARIS)-aided near-field (NF) uplink non-orthogonal multiple access (NOMA) secure communication system with an imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). In order to provide efficient resource utilization, a sum secrecy rate (SSR) maximization problem is formulated, where the combining vector at base station (BS), power allocation at each uplink user, and the HARIS phase profile are jointly optimized under the strict constraints of quality of service (QoS) requirement and limited power budget at each uplink user and HARIS considering norm-bounded CSI uncertainty. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as semidefinite programming (SDP), convex upper bound approximation, and semidefinite relaxation (SDR) to optimize all three design variables simultaneously. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum QoS corresponding to each user, the total power budget at uplink users, and maximum amplification factor at the HARIS. It is shown that the use of NOMA can achieve up to 45% higher performance compared to SDMA, OMA, and TDMA. It is also highlighted that, under the proposed algorithm with NF assumptions, the achieved average SSR is around 65% higher compared to the hybrid and far-field (FF) assumptions.

In vehicle-to-everything (V2X) networks, real-time telemetry is essential for enabling predictive analytics and fault detection in intelligent transportation systems. However, frequent wireless disruptions due to interference, mobility, and congestion lead to telemetry gaps that degrade downstream decision-making. To address this challenge, we propose a framework that enhances wireless telemetry robustness using large language models (LLMs) guided by digital twin-based context. Our system combines retrieval-augmented generation with environmental priors to recover high-dimensional, time-correlated telemetry streams lost during communication outages. We also integrate federated continual learning to maintain fault classification performance across non-i.i.d. V2X conditions without centralized data exchange. Extensive evaluations on real-world driving datasets with simulated wireless impairments show that our method significantly improves reconstruction fidelity, reduces degradation from multi-step gaps, and sustains long-term classifier stability. This work demonstrates how AI-driven semantic recovery mechanisms can improve the functional reliability of wireless V2X telemetry under dynamic and lossy network conditions.

The integration of dual-functional radar-communication (DFRC) systems with active reconfigurable intelligent surfaces (RIS) and movable antennas (MA) offers a powerful mechanism to meet the demands of sixth-generation (6G) networks. This paper presents a novel active RIS-aided integrated sensing and communication (ISAC) system where a DFRC base station (BS) equipped with a movable antenna array simultaneously serves multiple users and senses radar targets. The active RIS, incorporating low-power amplifiers, compensates for signal attenuation while reconfiguring the wireless environment. The MA array provides additional spatial degrees of freedom to enhance beam control, angular resolution, and robustness to blockage. To fully exploit this configuration, we formulate a joint beamforming and position optimization problem under SINR and power constraints. A successive convex approximation (SCA)-based alternating optimization algorithm is developed to address the non-convexities. Numerical results verify significant gains in communication throughput and radar beam sharpness compared to conventional passive RIS and fixed phased arrays, highlighting its potential for dynamic 6G scenarios such as intelligent surveillance and autonomous mobility.

Connected Autonomous Vehicles (CAVs) are poised to revolutionize intelligent transportation by enhancing road safety and reducing traffic congestion through real-time communication. However, this dependency introduces vulnerabilities to cyberattacks, especially in emergency scenarios where secure, low-latency communication is critical. While Quantum Key Distribution (QKD) provides quantum-secure key exchange, relying solely on it for all communication processes can introduce latency during key generation, which can be problematic in time-sensitive situations. To address this, we propose HyQCAN, a Hybrid Quantum-Classical Authentication Network that integrates QKD with Dynamic Basis Switching (DBS) for vehicle identity authentication of emergency vehicles in Vehicular Ad Hoc Networks (VANETs). QKD ensures quantum-secure encryption, while DBS enables real-time adaptation of the key exchange process based on communication urgency. HyQCAN achieves an optimal balance between security and responsiveness, with an Average Authentication Time (AAT) of 3.34 ms and an Average Key Generation Efficiency (AKGE) of 111.82 bps, effectively safeguarding critical VANET communications in high-priority situations.

Intelligent metasurfaces are emerging as a key technology for future wireless systems, enabling programmable control of electromagnetic wave propagation. Compared to conventional single-layer reconfigurable intelligent surfaces (RIS), stacked intelligent metasurfaces (SIM) introduce multiple reconfigurable layers to provide more flexible and precise beamforming. This paper investigates the integration of SIM into a downlink non-orthogonal multiple access (NOMA) system to improve spectral efficiency while maintaining low hardware complexity. The proposed system combines maximum ratio transmission (MRT) precoding at the base station, SIM-assisted analog beamforming, and NOMA-based power allocation. To maximize the system sum rate, we perform joint optimization of SIM phase shifts and user power levels through an alternating optimization (AO) framework, where each variable is updated iteratively while the other is held fixed. We evaluate three SIM-assisted strategies: NOMA, water-filling, and uniform power allocation. Simulation results demonstrate that the SIM-NOMA configuration achieves the 40% sum rate improvements, outperforming the other schemes while leveraging the low-cost wave-domain processing capabilities of SIM.

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided near-field (NF) driven downlink secure communication system under imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). We formulate a weighted sum secrecy rate (WSSR) while ensuring a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the base station (BS). Since the optimization problem is nonconvex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that jointly optimizes transmit beamforming at BS and phase shift at HRIS. Numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as HRIS reflecting elements, minimum QoS constraint, transmit power budget, and number of users. The dominance of RSMA over conventional multiple-access technologies such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) is also demonstrated.

This article proposes a holographic active reconfigurable intelligent surface (HARIS)-aided near-field (NF)-driven uplink non-orthogonal multiple access (NOMA) secure communication system under imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). To ensure efficient resource utilization, a sum secrecy rate (SSR) maximization problem is formulated by jointly optimizing the combined vector at the base station (BS), the power allocation for each uplink user, and the HARIS phase profile under strict quality of service (QoS) requirements and limited power budgets at both the uplink users and HARIS. Due to the non-convex nature of the problem, an alternating optimization (AO)-based algorithm is proposed, which uses semidefinite programming (SDP), convex upper bound approximation, and semidefinite relaxation (SDR) techniques to optimize all variables iteratively. Extensive simulations validate the performance and convergence of the proposed algorithm. In addition, the effects of system parameters, such as the number of HARIS elements, minimum QoS per user, maximum BS receive power, and the HARIS amplification factor, are investigated.

This study presents a dynamic UAV swarm framework to support ground networks in disaster zones. The framework leverages Generative AI (GenAI) for real-time hover point generation to guide waypoint-based UAV navigation and realistic task modeling, integrated with graph neural networks (GNN) for safe navigation and obstacle avoidance. A multi-agent graph reinforcement learning (MAGRL) mechanism optimizes UAV coordination, enhancing energy efficiency, task completion, and load balancing in response to environmental changes. The framework's graph attention mechanism further improves inter-UAV communication, enabling adaptive task allocation and efficient coverage of high-risk zones. Extensive simulations show that the integrated GenAI-GNN and MAGRL approach achieves superior performance in task completion, energy savings, and system utility, outperforming benchmarks including MADDPG, GCRL, PSO, and Greedy strategies in dynamic disaster scenarios.

This paper investigates an integrated sensing and communication (ISAC) system incorporating backscattering radio frequency identification (RFID) tag. In this configuration, a base station (BS) simultaneously serves multiple users through a communication beam while utilizing a sensing beam to detect the presence of an RFID tag. A joint beamforming design problem is formulated to maximize the sum-rate for the users while ensuring the minimum quality of service (QoS) for the tag detection and the minimum QoS of all communication users. To tackle the non-convex nature of objective function the Lagrangian dual transform technique is employed. Due to the coupling of variables, an alternating optimization (AO) based algorithm is proposed with guaranteed convergence. Through numerical simulations, we validate the effectiveness of our proposed algorithm. Additionally, we assess the impact of several key parameters on system performance, including the number of transmitting antennas at the BS, the available transmit power at the BS, the minimum QoS for the communication users, and the number of communication users.

This article investigates a near-field secure simultaneous wireless information and power transfer (SWIPT) network employing a non-orthogonal multiple access (NOMA) scheme. In this network, a base station equipped with an extremely largescale array antenna (ELAA) communicates with and transfers power to multiple single-antenna zero-energy devices (ZEDs) within the near-field region, while an eavesdropper attempts to wiretap the communication by intercepting the information signals. Specifically, we aim to maximize the overall secrecy sum rate of the ZEDs while ensuring a minimum energy harvesting criterion at the ZEDs. Consequently, a non-convex resource optimization problem is formulated and solved using an iterative approach, leveraging weighted sum-rate maximization via minorization-maximization (WSR-MM) with second-order cone programming (SOCP) transformation and general convex approximations. Finally, numerical results are presented to demonstrate the performance of the proposed near-field secure SWIPT system under varying network parameters.

This paper presents an optimization framework aimed at enhancing communication throughput for electric vehicles (EVs) operating on electrified roads (eROADs) while simultaneously ensuring quality of service (QoS). By integrating inductive coil-based wireless power transfer (WPT) with multiuser vehicular networks, our framework facilitates continuous inmotion charging alongside efficient communication management. To address transmission power and data rate constraints in a non-orthogonal multiple access (NOMA) uplink scenario, we tackle challenges posed by realistic mobility models, Doppler effects, and variability in network coverage. We develop a hybrid deep reinforcement learning (DRL) algorithm that integrates deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) techniques to maximize throughput. Simulation results indicate that our hybrid DRL algorithm surpasses traditional DDPG and PPO methods, achieving up to 13.74% higher throughput under low transmission power limits. Our approach manages WPT and communications efficiently, promoting EV operations in intelligent transport systems and aligning with green communication goals.

This paper introduces a network architecture that integrates the space-air-ground integrated network with mobile edge computing (MEC) and orbital edge computing to advance sixth-generation communication systems. The proposed system employs unmanned aerial vehicles equipped with reconfigurable intelligent surfaces and satellite-based MEC to optimize resource management in complex, dynamic environments. By efficiently managing resources such as bandwidth and computational power at both base stations and low Earth orbit satellites, while making offloading decisions, the system aims to minimize utility costs while meeting stringent performance requirements. We utilize a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) algorithm to solve the formulated nonlinear programming problem, enabling dynamic and adaptive resource management. The LSTM-enhanced DDPG improves convergence speed by 44.44 % compared to conventional DDPG, significantly enhancing cost efficiency. Simulation results validate the robustness of the proposed method against state-of-the-art approaches.

The integration of the Internet of Things (IoT) with meta-materials advances both communication and sensing technologies. Using Meta-IoT sensors, environmental data can be acquired by analyzing the frequency response from reflected signals. In this study, we introduce a dual-purpose Meta-IoT sensor capable of simultaneously sensing environmental conditions and enabling communication. Machine learning (ML) plays a key role in optimizing sensor design and power management within these meta-material IoT systems. Hence this work leverages machine learning (ML) to optimize sensor design and power management in Meta-material IoT (Meta-IoT) systems. Specifically, Gaussian process regression (GPR) is used to explore and optimize sensor structures, while reinforcement learning adapts power management strategies dynamically. Simulation results highlight that the ML-driven methods enable adaptable and efficient Meta-IoT systems.

In this study, we propose SCF6, a novel semantic communication framework for 6 G -enabled vehicular networks tailored to ultra-reliable low-latency communication (URLLC) scenarios. SCF6 integrates semantic encoding/decoding with conventional channel processing, optimizing transmission by focusing on data meaning. Leveraging BERT (bidirectional encoder representations from transformers)-based natural language processing, it ensures high semantic similarity between transmitted and received messages. To maximize semantic spectrum efficiency (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under strict URLLC constraints, we design a multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework. MAHASDRL coordinates resource allocation and spectrum sharing, embedding hierarchical attention at both semantic and channel levels to enhance decision-making, optimize power control, and reduce interference. Simulations demonstrate SCF6's superiority over traditional DRL methods in spectrum efficiency, reliability, and latency, proving effective for dynamic urban vehicular networks.

This work proposes the integration of the holographic active reconfigurable intelligent surface (HARIS) into a multi-user uplink near-field-driven wireless communication system. In order to provide efficient resource utilization, a sumrate maximization problem is formulated, where the equalizer design, the power allocation at each user, and the HARIS phase profile are jointly optimized under the strict constraint of QoS requirement and limited power budget at each user and HARIS. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR) to simultaneously optimize the equalizer at the BS, beamforming at the HARIS, and power allocation at each user. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum quality of service (QoS) constraint corresponding to each user, maximum receive power at the base station (BS), and maximum amplification factor.

With the continuous and fast-changing land-scape in communication networks and artificial intelligence (AI), the researchers are interested in expedited standardization and realization of 6G networks. Federated learning (FL) is one of the paradigms that allows the 6G networks to support a diverse range of devices. Very few studies address the problem of flexibility and heterogeneity for AI network architectures in FL paradigm, that could be a potential key changer for standardization and realization of 6G networks. However, they either consider width-only or depth-only to provide flexibility support. Furthermore, the existing studies do not address the problem of weight scale variation while performing the global model aggregation at the server side. In this regard, we propose flexible federated learning (FLEXFL) for the support of heterogeneous AI network architectures in 6G communication systems. The proposed network not only considers the width but also the depth of the network architecture to make it compliant with the global model aggregation. We also address weight scale variation (WSV) while updating the global model with weight normalization, which is one of the problems associated with existing studies. We perform experimental analysis on two publicly available datasets and a few network architectures to show the efficacy of the proposed approach. The results reveal that the FLEXFL outperforms existing state-of-the-art works in both the IID and non-IID settings, accordingly.

This study analyzes the performance of a two-hop communication system that integrates free-space optical (FSO) transmission with intelligent reflecting surface (IRS)-aided radio frequency (RF) transmission. This analysis focuses on the transmission within a non-orthogonal multiple access (NOMA) system over heterogeneous channels: the source-to-relay FSO channel follows a Fisher-Snedecor$F$distribution, the relay-to-users RF channels are modeled as Rayleigh distributions, the relay-to-IRS and IRS-to-users RF channels are characterized by Rician distributions. A closed-form expression for the outage probability has been derived and approximated as a Gamma distribution using the moment-generating function under imperfect successive interference cancellation. Notably, the outage probability expressions and their approximation closely align with our simulation results, thus validating the accuracy of our findings. Additionally, an asymptotic expression for the outage probability has been derived, particularly in the FSO transmit SNR regime, and the corresponding diversity order is evaluated. The impact of various system parameters, such as turbulence, on the rate of decay of the outage probability is explained through the diversity order.

Smart wireless communications enabled by reconfigurable intelligent surfaces (RISs) have gained significant research interest in the areas of localization and sensing over the past few years. This paper investigates an unconventional approach for 3D direction-of-arrival (DoA) estimation of multiple aerial user targets using an RIS-based communication architecture. In particular, the measurements required for DOA estimation at the receivers are optimized through a deep reinforcement learning framework. The core of the proposed method lies in formulating the DoA estimation problem as a Markov decision process (MDP), which is optimized via a proximal policy optimization algorithm for its optimization. Considering a practical RIS setup with 2-bit states at each unit element, we demonstrate significant improvements in DoA estimation accuracy, in terms of reduced root mean squared error (RMSE) for various simulation scenarios of the system.

Visible light communication (VLC) is an energy-efficient, green, and low-cost technology for high-speed next-generation communication systems. However, it has been observed that the performance of a VLC-based system is limited due to the light-emitting diode's (LED) nonlinear characteristics, low coverage area, and loss of the VLC signal due to the absence of a direct link between the transmitter and receiver caused by blockages present in the environment. In addition, the secrecy sum rate (SSR) of a VLC system is compromised due to the presence of an eavesdropper. To address the problem of dead zones and low coverage area, an optical simultaneously transmitting and reflecting intelligent surface (OSTAR-RIS) is proposed in the literature. For SSR maximization, the particle swarm optimization (PSO) method is employed and compared with the benchmark exhaustive search method. For implicit channel estimation and direct symbol detection, an LSTM-based algorithm for a nonlinear OSTAR-RIS VLC system is proposed. Simulations indicate superiority of the proposed optimization and implicit channel estimation technique over benchmark schemes.

In this study, we introduce a digital twin (DT)-assisted IoV framework that combines a semi-synchronous adaptive federated learning (AdFL) method with multi-agent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAE). This framework optimizes partial task offloading across distributed mobile edge computing (MEC) servers, ensuring scalable and efficient decision-making in diverse vehicular networks. By continuously reflecting the real-time conditions of vehicles and roadside units (RSUs), the DT framework ensures precise resource distribution and adaptive task handling. To handle the complexity of dynamic environments, we develop a global model that includes transformer layers in the federated learning (FL) process, which captures long-range dependencies. A semi-synchronous aggregation mechanism is introduced to maintain a balance between timely updates and model quality. The adaptive federated multi-agent reinforcement learning (AF-MARL) algorithm enables decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost and energy use, reducing delays, and improving task completion rates. Comprehensive simulations show the framework's effectiveness compared to existing methods, emphasizing its potential to revolutionize real-time decision-making in IoV networks.

Uncrewed aerial vehicles (UAVs) are essential for providing communication and computation services in disaster recovery scenarios where traditional infrastructure is compromised. However, challenges related to energy efficiency, real-time adaptability, coverage, load balancing, and safe navigation persist, particularly in dynamic disaster environments. In this study, we propose a comprehensive framework that integrates generative AI (GenAI) with graph neural networks (GNNs) to dynamically generate hover points for waypoint-based UAV navigation and realistic task generation based on environmental conditions. The GNN-based collision avoidance mechanism further ensures safe navigation by allowing UAVs to avoid obstacles and no-fly zones while coordinating with neighboring UAVs in real time. To optimize UAV swarm operations, we introduce a multiagent graph reinforcement learning (MAGRL) framework, enabling UAVs to maximize overall system utility by refining hover point selection, task allocation, and load balancing in response to environmental changes. A graph attention mechanism enhances UAV coordination, improving communication efficiency and decision-making. Extensive simulations show that the proposed GenAI-GNN and MAGRL framework significantly outperforms existing methods in task completion, energy efficiency, and overall system utility in disaster recovery scenarios.

As the proliferation of Internet of Things (IoT) devices accelerates, next-generation wireless networks face unprecedented demands for secure, efficient, and scalable communication frameworks. This paper investigates the integration of non-orthogonal multiple access (NOMA) with reconfigurable intelligent surfaces (RIS) and full-duplex (FD) operations to address these challenges while mitigating the adverse effects of residual hardware impairments (HWI) that cause signal distortion. The proposed RIS-aided FD-NOMA system is designed to enhance resilience and secrecy in IoT communication networks, optimizing the secrecy rate while adhering to power constraints for active beamforming at the base node (BN) and unit-modulus requirements for passive beamforming via RIS. Employing an alternate optimization (AO) framework, the complex joint optimization problem is divided into tractable subproblems solved through generalized convex approximations to achieve near-optimal solutions. Numerical results validate the superiority of the proposed model, demonstrating substantial performance improvements over conventional IoT systems without RIS support or with half-duplex NOMA protocols. The findings underscore the transformative potential of RIS-aided FD-NOMA systems in securing IoT networks against hardware imperfections while meeting their stringent connectivity and security demands.

Backscatter communication (BackCom) holds immense potential for enhancing the capabilities of energy-constrained Internet of Things (IoT) devices and supporting a wide range of applications. Nonorthogonal multiple-access (NOMA) schemes are advantageous for meeting the demands of massive connectivity in next-generation communication networks, and NOMA-aided BackCom can leverage the benefits of both BackCom and NOMA. This article covers the basics of BackCom, explores different types of BackCom systems, and explains how NOMA can be integrated with BackCom, where various crucial issues, including design principles, channel state information (CSI) estimation, node pairing schemes, and reflection coefficient (RC) designs, are illustrated. Furthermore, this survey evaluates the effectiveness of different network architectures for NOMA-aided BackCom, especially in IoT networks, and identifies potential research directions and future trends in this area. The key considerations for designing and optimizing NOMA-aided BackCom systems in real-world scenarios are also highlighted in this survey. In addition, by reviewing the existing literature and research on NOMA-aided BackCom, this article provides valuable insights for researchers and practitioners interested in this emerging wireless communication technique.

This article characterizes the performance of mixed free-space optical (FSO)/active intelligent reflecting surface (IRS)-aided nonorthogonal multiple access (NOMA) communication in the presence of imperfect channel state information (CSI) and imperfect successive interference cancelation (SIC). The detrimental effects of atmospheric turbulence on the FSO communication link, which affects end-to-end communication, are also presented. Considering practical implementation, we illustrate the impact of phase quantization error, imperfect SIC, and imperfect CSI on the outage performance. Our numerical analysis indicates that the outage performance can be significantly improved by utilizing multiple transmit apertures through transmit aperture selection at the base station and employing multiple transmit antennas via maximum ratio transmission at the relay. In addition, the numerical results demonstrate that performance improvements are achieved by mitigating double path loss through an active IRS compared to a passive IRS. The closed-form expression for the outage probability is derived using methods of moments and then verified through Monte Carlo simulations. To gain more valuable insights, asymptotic expressions are also provided for high RF transmit power and high FSO transmit power scenarios. Consequently, diversity orders are derived for an asymptotic study of the outage performance of the considered NOMA network.

In this work, we investigate the performance of a multi-active reconfigurable intelligent surface (ARIS)-aided full-duplex (FD) secure Internet of Things (IoT) network with imperfect Channel State Information (iCSI) in the presence of an eavesdropper (Eve). We formulate a power minimization problem while ensuring the minimum Quality of Service (QoS) of all the nodes within available resource constraints considering the norm-bounded iCSI. To tackle the nonconvex nature of the formulated problem, we adopt analytical methods, such as semidefinite programming, S-procedure, and general sign-definiteness, and propose an alternating optimization (AO)-based algorithm that jointly optimizes the receive and transmit beamforming at Alice, power allocation at each uplink user, and active beamforming at ARIS. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of ARISs, compared to its passive RIS (PRIS) counterpart, toward achieving a robust and secure FD system is demonstrated. Finally, we discuss the impact of key parameters, such as maximum amplification factor, RIS, and CSI error on the performance of the considered system.

Achieving real-time decision-making and efficient resource management in dynamic, large-scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent complexity and scale. To address this, we propose a digital twin (DT)-assisted IoV framework that integrates a novel semi-synchronous adaptive federated learning (AdFL) approach with multiagent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAEs). The framework optimizes partial task offloading across distributed mobile-edge computing (MEC) servers, ensuring scalable, efficient, and accurate decision-making in heterogeneous vehicular networks. By continuously mirroring the real-time states of vehicles and roadside units (RSUs), the DT framework enables precise resource allocation and adaptive task management. To tackle the complexities of dynamic environments, we design a global model with transformer layers embedded in the federated learning (FL) process, capturing long-range dependencies. A novel semi-synchronous aggregation mechanism is introduced to balance timely updates with model quality. The proposed adaptive federated multiagent reinforcement learning (AF-MARL) algorithm facilitates decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost, and energy efficiency, reducing delay, and improving task completion rates. Extensive simulations demonstrate the effectiveness of the proposed framework against other existing approaches, highlighting its potential to transform real-time decision-making in IoV networks.

Holographically driven active reconfigurable intelligent surface (HARIS), leveraging densely packed subwavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multiuser uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of Quality-of-Service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the nonconvex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques, such as minimum-mean-square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm’s efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance.

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided downlink network for efficient near-field communication. We formulate a sum-rate maximization problem that jointly optimizes the transmit beamforming at the base station (BS), the common rate of each receiving internet of things (IoT) node, and beamforming at the HRIS transmission design to ensure a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the BS. Since the optimization problem is non-convex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that efficiently solves the joint optimization problem utilizing analytical tools such as the successive convex approximation (SCA). Various numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as reflecting elements, minimum QoS constraint, transmit power budget, and number of IoT nodes. The dominance of RSMA over the counterpart, non-orthogonal multiple access (NOMA), is also demonstrated. It is shown that the use of RSMA can achieve up to 96% higher performance compared to NOMA. It is also highlighted that with near-field assumptions, the average sum rate increases around 69% compared to hybrid 66% and far-field model 64%.

This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.

This paper investigates a cooperative Internet of Things (IoT) non-orthogonal multiple access (NOMA) network comprising of a multi-antenna base station (BS), a near IoT user (NU) with full-duplex capabilities, and multiple far IoT users (FU). The NU utilizes the power-splitting (PS) energy harvesting (EH) protocol and augments the harvested energy with a little energy from its battery to assist the FU. Considering the novel cooperative NOMA/non-cooperative (C-NM/NC) switching, the impact of successive interference cancellation (SIC), channel state information (CSI) errors, and nonlinear EH, closed-form expressions are derived for average blocklength error rates (BLER) of both users in finite blocklength regime. Utilizing the derived BLER expressions, the Goodput, Reliability, Latency, and battery power efficiency are expressed in closed form. We then demonstrate that the CSI errors severely degrades the performance of both the users. Monte Carlo simulations validate the accuracy of the derived analytical expressions. We also establish that a significantly better maximum performance is attained at the FU, given a target NU Goodput by the careful choice of NOMA and EH parameters. C-NM/NC switching offers a higher EE and better self-interference immunity. Results indicate that the choice of blocklength is crucial for efficient system performance.

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.

The increasing complexity of real-time healthcare necessitates intelligent systems for dynamic data management and personalized assistance. This paper proposes a novel dual-LLM framework that integrates large language models (LLMs) into wireless healthcare networks. The first LLM powers an interactive artificial intelligence module (IAIM) embedded within a mobile edge computing (MEC) environment, which dynamically optimizes user-specific data routing and reconfigurable intelligent surface (RIS) configurations via a modified proximal policy optimization (PPO) algorithm. A novel Greedy Look-Ahead Algorithm (GLAA) is introduced for real-time path selection based on signal strength, emergency factors, and user-specific parameters. The second LLM, utilizing a retrieval-augmented generation (RAG) approach, serves as a personalized healthcare chat assistant that delivers context-aware patient support using real-time and historical data. Simulation results demonstrate that the proposed IAIM achieves a 9.6% reduction in network overhead compared to manual modeling and reduces latency by up to 52.5% over baseline PPO approaches, thus enabling enhanced user experience and responsiveness in healthcare systems.

One of the disruptive communication technologies for sixth-generation (6G) wireless networks is cell-free massive multiple-input multiple-output (CF-mMIMO), which is capable to control inter-cell interference in MIMO systems. This paper investigates the performance of a full-duplex (FD) CF-mMIMO systems with practical limited-capacity fronthaul links. The proposed system employs a large number of M distributed FD APs, arbitrarily distributed$K_{d}$downlink (DL) and$K_{u}$uplink (UL) half-duplex (HD) single-antenna equipped user terminals (UEs), and a central processing unit (CPU). To exploit the energy efficiency and potential throughput gains of FD systems, each AP is linked to the CPU through a fronthaul link with limited capacity that handles the quantized UL/DL data to/from the CPU. Each AP is expected to support K HD UEs on the same spectrum resource, where$K = (K_{u} + K_{d})$. Imperfect channel state information and the mobility of the UEs are also considered. A closed-form expression for the outage probability is derived using the optimal uniform quantization and maximum-ratio combining/maximum-ratio transmission considering the Welch-Satterthwaite approximation. Additionally, the asymptotic and infinite-M outage expressions for the proposed system are analytically studied and verified via Monte Carlo simulation. Simulation results demonstrate the relationship between the improved outage performance and uniform quality of service (QoS) for all UEs. Moreover, this analysis provides valuable insights into the behavior of FD-CF-mMIMO system and underscores the importance of providing a uniform QoS to all UEs in improving the overall performance of the system.

In the context of ultra-reliable low-latency communication (URLLC) in Internet-of-Things (IoT) networks, conventional half-space coverage limits the flexibility of reconfigurable intelligent surface (RIS) deployment. To overcome these constraints, this paper makes use of active simultaneously transmitting and reflecting RIS (STAR-RIS), which is seamlessly integrated into digital twin (DT) and mobile edge computing (MEC) frameworks. Our primary research objective is to achieve full-space coverage by enabling simultaneous transmission and reflection of the signals while improving uplink data transmission from IoT URLLC user nodes (UNs) to the base station (BS) with the assistance of active STAR-RIS, even in the presence of imperfect channel state information (CSI). We formulate the problem of minimizing total end-to-end (e2e) latency, computed using the alternating optimization (AO) algorithm. Subsequently, we have evaluated the performance of the AO algorithm against the stochastic gradient descent (SGD) algorithm, which serves as the benchmark solution. The simulation outcomes delineate a performance evaluation under perfect and imperfect CSI scenarios. The AO algorithm outperforms SGD with latency reductions of 19.7% at$N=32$and 20.4% at$N=64$. Increasing N from 32 to 64 results in a 39.3% latency reduction for AO, surpassing SGD’s 38.8%. However, the SGD algorithm consistently exhibits lower computational complexity compared to the AO algorithm. Additionally, the energy splitting mode achieves the system’s total e2e latency reductions of 28.4% over the mode switching mode and 11.04% over time switching mode. Furthermore, active STAR-RIS optimal beamforming (ARO) achieves$\approx 10$% latency reduction over the predictive optimal beamforming (PRO), which itself surpasses active STAR-RIS with random beamforming (ARR) by$\approx 9$%. This comparison considers key factors such as the power budget, the number of RIS elements, the caching capacity of the edge computing server (ECS), the number of IoT UNs, the minimum transmission rate, and maximum transmit power at BS of active STAR-RIS.

The paper evaluates the ergodic sum capacity, outage performance for communication users, and the detection probability, beampattern gain for sensing targets in a simultaneous transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) aided integrated sensing and communication (ISAC) system. The rate splitting multiple access (RSMA) technique and maximal ratio transmit beamforming at the multi-antenna base station have been explored. Closed-form expressions for the ergodic sum capacity and outage probability of the STAR-IRS-aided RSMA ISAC system are derived through moment methods. The derived expressions are validated through Monte Carlo simulations. Additionally, to provide deeper insights into the diversity orders of the RSMA ISAC system, we conduct an asymptotic outage probability analysis in the high signal-to-noise ratio regime. The effect of the number of base station antennas and STAR-IRS elements on outage performance has been demonstrated, along with an explanation of the underlying reasons through diversity gain. Furthermore, it shows that the implementation of STAR-IRS significantly boosts the system’s ergodic sum capacity compared to traditional reflecting-only IRS. Additionally, the RSMA technique delivers more substantial performance improvements than the non-orthogonal multiple access (NOMA) in high transmit SNR conditions while demonstrating comparable performance in low transmit SNR scenarios. A comparison between energy splitting and mode switching STAR-IRS has been conducted under both ideal and random phase shift conditions. A trade-off analysis between communication and sensing rates is presented. Additionally, the accuracy of target sensing is evaluated by measuring the mean square error (MSE) in beampattern gain matching. The impact of quantization levels for phase shift of STAR-IRS on outage probability has also been addressed. Finally, the effects of power allocation for sensing on detection probability and beam pattern gain are also presented.

This paper investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink (dl) communications with a primary focus on maximizing information secrecy by considering the channel state information (CSI) error. Acquiring perfect CSI is particularly challenging due to the unavailability of radio frequency chains at the STAR-RIS, the inherent impact of noise and interference on the CSI estimation, as well as non-collaborative nature of the eavesdroppers. In particular, we tackle the worst-case robust beamforming design problem to maximize the sum secrecy rate of the system while considering transmit power limitations, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. To tackle the resulting non-convex problem, we employ the S-procedure as an initial step to approximate semi-infinite inequality constraints. Subsequently, we leverage the alternating optimization with a line search framework to update the precoder and phase shift matrix iteratively. Furthermore, we extend our solution to address the non-convexity by leveraging a deep reinforcement learning (DRL) multi-agent (MA) framework based on Markov decision process. We also analyze practical phase shifts and the effect of direct links to showcase the practicality of our approach. Simulation results confirm STAR-RIS’s significant performance edge, exhibiting approximately 27.1% higher secrecy in conventional optimization and around 35.4% in the MA-DRL context compared over the conventional RIS. Moreover, our proposed MA-DRL approach surpasses single-agent schemes by about 8.6% in the case of proximal policy optimization and 19.9% in the case of deep deterministic policy gradient, emphasizing the benefits of the MA framework with STAR-RIS.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several low mobility targets. We formulate a sum-rate optimization problem that accounts for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory, and transmit beamformer for integrated sensing and communication (ISAC). Subsequently, we introduce a transfer learning based approach combining with deep deterministic policy gradient (DDPG) to enhance the overall data rate while minimizing the time it takes for users to transmit data. Additionally, we present an alternating optimization (AO) algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and offers a solution that is very close to optimal. Finally, we showcase the superiority of the proposed scheme through Monte Carlo simulations. Also, we have compared the performance with perfect channel state information (CSI) counterpart. The outcomes of simulations confirm the theoretical analysis and demonstrate the efficiency of the proposed framework. Additionally, the results reveal the advantages of incorporating HRIS aided UAV assisted ISAC in improving the quality of both communication and sensing performance.

In this work, we consider a cooperative communication network wherein a base station (BS) utilizes a simultaneous wireless power and information transfer (SWIPT) energized full duplex-decode and forward relay to communicate with a downlink user. The relay augments the harvested energy with a bit of energy from its battery. Assuming a practical nonlinear energy harvesting (EH) model, we derive approximated closed-form expressions for the end-to-end blocklength error rate (BLER) under the finite blocklength (FBL) regime for both power splitting (PS) and time switching (TS) protocols. We then derive a high-SNR approximated expression for end-to-end BLER to demonstrate the interplay of the battery energy, blocklength, and EH parameters on the system’s performance. We also analytically establish the convexity of the BLER with respect to the relay’s battery energy. Using the expression for the BLER, we derive expressions for the goodput and battery energy efficiency in approximated closed-form. Moreover, for a desired target BLER requirement, we show that, by carefully choosing the TS/PS parameter, the required relay’s battery energy can be minimized. The accuracy of the derived analytical expressions is validated using Monte Carlo simulations. Finally, we discuss the impact of key parameters such as transmit power, total available blocklength, nonlinear EH, TS, and PS parameters, and battery energy on the network’s performance.

This paper introduces a novel multi-agent federated deep reinforcement learning (MA-FDRL) framework designed to minimize vehicular task offloading latency in electrified road (eROAD) environments. The solution integrates inductive coil-based wireless power transfer (WPT) systems with full duplex multiple input and multiple output (MIMO) vehicular networks, enabling continuous charging and reducing computational delays for electric vehicles (EVs) on eROADs. The MA-FDRL framework optimizes the offloading of vehicular tasks, with support from base stations (BS), unmanned aerial vehicles (UAVs), and satellites, while ensuring data privacy through differential privacy techniques. By intelligently distributing resources across these supporting entities, the framework enhances the efficiency of task processing. Key challenges such as dynamic wireless charging, intermittent BS coverage, and privacy-preserving task offloading are addressed using a comprehensive WPT framework, a mobility model, and a differential privacy-enhanced MA-FDRL algorithm. The proposed MA-FDRL solution effectively reduces the latency of vehicle task offloading by 17.05% over proximal policy optimization (PPO) algorithm, ensures balanced task distribution between edge servers, and offers a scalable and privacy-preserving approach for future autonomous electric vehicles and connected wireless environments.

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments.

This article explores the critical aspect of electronic hardware reliability analysis in reconfigurable intelligent surface (RIS)-aided networks within the context of sixth-generation (6G) communications. Recognizing the potential vulnerabilities of metasurfaces to environmental factors, we highlight the continuous hardware impairments that can significantly impact the electromagnetic properties of RISs, reducing their lifetime. Extending the life cycle of RISs is strategically important, especially in mission-critical ultra-reliable wireless applications where system failures can result in significant costs and, in extreme cases, necessitate structural replacements. Accordingly, we investigate the nonresidual continuous hardware degradation of RISs through a stochastic process and optimize maintenance strategies using statistical information to extend the RIS system's lifespan. The optimal life expectancy of the RIS system with systematic maintenance concerning the observed impairment level is demonstrated through analytical results. The findings indicate that the information-based framework can significantly extend the expected life of a RIS system by postponing maintenance. Furthermore, a comprehensive mathematical framework for reliable communication is introduced, whereby the distribution of the received SINR is determined in the presence of hardware impairment due to imperfect maintenance. Through extensive numerical simulations, the efficacy and robustness of the proposed framework under hardware impairments are illustrated.

This paper presents a state-of-the-art analytical framework aimed to enhance spectral efficiency in satellite and terrestrial integrated networks (STINs), utilizing reconfigurable intelligent surface (RIS) within the realm of integrated sensing and communication (ISAC). Our methodology pivots on a pioneering federated deep reinforcement learning strategy that introduces new ground beyond conventional optimization techniques to tackle the intricate problem of non-convex resource allocation. The approach leverages federated learning to dynamically adapt to network changes, enabling efficient resource management and ensuring compliance with beamforming designs, multiple target signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements through an effective feedback loop. In particular, we propose a federated deep deterministic policy gradient (F-DDPG) algorithm across multi-agent systems that outperforms existing federated deep Q-network (F-DQN), centralized, and traditional DDPG and DQN methods. The empirical findings underscore the efficiency of the federated algorithms, which closely align with the performance of centralized models while markedly reducing execution time, thus achieving an optimal synergy between operational efficiency and system performance. Simulation results highlight the remarkable advantages of optimal RIS configurations, showcasing a performance increase of 54.2% over random RIS setups and a remarkable 76.8% enhancement compared to scenarios without RIS, underscoring the transformative impact of our federated learning approach. Additionally, our study evaluates the impact of channel estimation errors and interference, confirming the robustness of our approach and its potential to optimize ISAC-enabled STINs.

The paper explores hybrid free-space optical (FSO) and active intelligent reflecting Surface (IRS)-aided radio frequency (RF) transmission in non-orthogonal multiple access (NOMA) networks to improve outage performance of internet of things (IoT) devices under imperfect channel state information (imCSI) and imperfect successive interference cancellation (imSIC). The superposed signal is transmitted from the source to the relay over FSO link exploiting the line of sight (LOS) path modeled as Gamma-Gamma distribution. Whereas RF signals transmission from the relay to IoT devices are boosted through an active-IRS. The multiplication factor of path-loss is prominent in the presence of a direct path from the relay to IoT devices. The active-IRS can mitigate the issue. In this paper, the direct path is modeled using a Rayleigh distribution, while the indirect paths via the Active-IRS are modeled using a Rician distribution. The paper demonstrates the impact of active noise introduced by the Active-IRS on outage performance. The closed-form expression for outage probability is derived under imCSI and imSIC, taking into account active-IRS noise. Additionally, it conducts an asymptotic analysis in a high signal-to-noise ratio (SNR) regime. The analytical results closely align with Monte Carlo simulation results, validating the accuracy of the findings.

This paper presents a novel analytical framework for minimizing transmit power in satellite and terrestrial integrated networks using reconfigurable intelligent surface (RIS) technology within integrated sensing and communication systems. We employ a cutting-edge federated deep reinforcement learning approach, utilizing a federated deep deterministic policy gradient (F-DDPG) algorithm, to tackle the complex non-convex power minimization problem effectively. The proposed F- DDPG approach surpasses the federated deep Q-network (DQN), traditional DDPG, and DQN techniques by dynamically adapting to network changes, enabling efficient resource management and compliance with beamforming designs, multiple target and user signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements. Simulation results confirm that the use of RIS can significantly lower power requirements at the base station and maintain a critical balance between efficient power management and strategic resource allocation.

As we advance towards sixth-generation (6G) communications, the integration of dual-functional radar and communication (DFRC) systems with active reconfigurable intelligent surfaces (RIS) emerges as a promising solution to enhance spectrum efficiency. This work introduces an innovative DFRC system enhanced by an active RIS, capable of amplifying and phase-shifting signals to improve communication quality. We propose an optimization algorithm that maximizes combined data rates while considering constraints on radar probing power and power budgets for both DFRC and active RIS. Employing techniques such as weighted minimum mean square error (WMMSE) and fractional programming, our simulation results demonstrate that active RIS-enhanced DFRC systems significantly outperform passive RIS and non-RIS setups in terms of Weighted Sum Rate (WSR). Further analyses reveal that active RIS effectively mitigates multiplicative fading and enhances signal directivity, with performance gains influenced by the number of RIS elements, total system power, and radar detection power ratio. This work highlights the transformative potential of active RIS in optimizing next-generation wireless networks.

This paper investigates the deployment of a full-duplex integrated sensing and communication (ISAC) system for task offloading service to serve ultra-reliable low-latency communications (URLLC), significantly enhanced by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Utilizing a non-orthogonal multiple access frame-work, this study extensively addresses the challenges associated with latency-sensitive task offloading from information receivers (IRs) to a mobile edge computing platform. We introduce a novel and sophisticated multi-agent deep reinforcement learning (MA-DRL) approach aimed at minimizing latency in task offloading under a variety of stringent ISAC-URLLC network constraints, including imperfect channel state information. The proposed MA-DRL operates on decentralized execution while maintaining centralized training, using multi-actor-critic networks to enhance learning and performance. The innovative reward decentralization framework in the present MA-DRL optimizes downlink and uplink communications through dynamic power allocation, precise beamforming, and intelligent phase shift management facilitated by the STAR-RIS. The proposed MA-DRL framework significantly outperforms existing multi-agent DRL algorithms, demonstrating substantial gain in reward maximization (i.e., linked to offloading latency minimization) by 27.46% and 52.73%.

Vehicular crowdsensing (VCS) encounters challenges within social Internet of Vehicles networks, including interdependent behaviors and the necessity for long-term sensing strategies that balance energy efficiency and delay tolerance in dynamic settings. To tackle these obstacles, this study explores a VCS model tailored for social IoV networks, considering dynamic environmental parameters. We further develop a utility model that seamlessly integrates data-quality aware functional and social incentives for each vehicle, ensuring optimal task payoff, efficient energy usage, and minimized processing time within the dynamic social IoV environment. Additionally, we introduce a non-cooperative game between vehicles and propose a multi-agent deep reinforcement learning (DRL)-based solution for the dynamic VCS framework. This enables vehicles to autonomously adjust sensing levels, maximizing both individual and collective utility. Finally, through comparative simulations, we demonstrate the effectiveness of our approach in comparison to baseline methods.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO) based approach for the DRL-based decision-making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, non-uniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that mPPO outperforms conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential for real-world applications.

In this paper, we consider rate splitting multiple access (RSMA) based simultaneous refracting/transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS) aided downlink wireless network for the data transmission from an access point (AP) to two Internet-of-Things devices (IoDs) over Nakagami fading channel. AP executes the integrated sensing and communication (ISAC) principle to eliminate the issue of undesired interference between a communication system and a target. RIS association with RSMA is used in the system to enhance the quality of signal at a higher sum rate. To evaluate the performance of the proposed system, we analyze the outage probability and ergodic sum rate. Simulation results show the impact of the diversity order of Nakagami parameter, and configurable elements of RIS on the system performance along with the sensing performance of AP. Almost 10% rate enhancement is achieved through RSMA compared with non-orthogonal multiple access (NOMA) technique at 10 dBm transmit power.

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-MetaFL, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-MetaFL is strengthened by the quantum sequential-training-program (Q-STP) algorithm, a quantum-based sequential training program that transforms model training, reducing communication costs and adeptly managing vehicle states. Complementing this, the quantum vehicle-context-grouping (Q-VCG) mechanism groups vehicles based on contextual data similarity, effectively tackling the complexities of data heterogeneity. The synergy of Q-STP and Q-VCG culminates in the QV-MetaFL algorithm, a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Additionally, our research introduces an innovative composite loss function that amalgamates classical loss metrics with quantum parameter regularization, deftly addressing quantum sensitivity to noise. The effectiveness of the QV-MetaFL framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.

In the ever-evolving landscape of next-generation wireless communication systems, the need for high data rates, seamless connectivity, and energy efficiency continues to increase. To meet these demands, the integration of emerging technologies such as cell-free massive multiple-input multiple-output (CFm-MIMO) and reconfigurable intelligent surfaces (RIS) has gained significant attention. This paper presents a comprehensive performance analysis of a downlink active RIS-assisted CFm-MIMO system in the context of user terminal (UT) mobility, emphasizing the critical aspect of constrained fronthaul capacity. The paper employs a rigorous analytical framework to determine the outage performance of the proposed system considering imperfections in the channel state information (CSI). The findings presented in this paper demonstrate the impact of the number of RIS elements ($N$), quantization parameters, UT mobility, scattering models, and imperfect CSI on the outage probability. It is revealed that by increasing the$N$from 8 to 32 will significantly decreases the OP by 99.75%. Moreover, it is interesting to observe that the choice and optimization of quantization parameters remain consistent regardless of RIS presence.

This paper explores a cutting-edge terahertz (THz) integrated sensing and communication system (ISAC) that utilizes active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (A-STAR-RIS). The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicles, and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system features a dual-function radar and communication multiple antenna base station (BS), serving both communication and target sensing functions concurrently through an A-STAR-RIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, A-STAR-RIS reflection and transmission beamforming matrices, vehicular unit (VU) mobility correlation parameters, and radar receive filter. Given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables, traditional optimization methods prove challenging. To overcome this, we propose a machine learning (ML) based deep deterministic policy gradient (DDPG) algorithm. Our simulations validate the substantial benefits of A-STAR-RIS over conventional benchmark scenarios.

This paper presents a novel design for a mobile edge computing (MEC) service that integrates digital twin technology with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). This configuration leverages edge intelligence, aiming to strengthen ultra-reliable and low-latency communications (URLLC) within Internet-of-Things (IoT) frameworks. We explore the uplink data transmission path from singular-antenna IoT URLLC nodes (UNs) to a multi-antenna base station (BS) facilitated by an active STAR-RIS. Our focus is on framing an end-to-end (e2e) latency reduction strategy for the presented system. Due to the inherent non-convexity of this problem, we propose an efficient alternating optimization (AO) algorithm to get a solution. This algorithm decomposes the main problem into five distinct sub-problems: transmit beamforming design, optimization of caching and offloading policies, joint communication and computation optimization, and enhancement of active STAR-RIS beamforming. An extensive set of simulation outcomes indicates that our DT-enhanced optimal-phase STARRIS approach consistently surpasses benchmark methods, particularly when accounting for variables such as power constraints, the number of RIS elements, the caching capacity of the edge computing server (ECS), and the number of IoT UNs.

In this paper, we present an innovative approach to spectrum management in cognitive radio networks (CRNs) aimed at serving ultra-reliable low-latency communication (URLLC) enabled secondary users (SUs). Unmanned aerial vehicles (UAVs) are deployed for accurate and reliable spectrum sensing (SS), enhancing cooperative spectrum sensing (CSS) effectiveness. A distinctive aspect of our methodology is the integration of digital twin (DT) technology, which, to our knowledge, has not been explored previously in the context of CRNs for bandwidth assignment to URLLC-enabled SUs. This integration facilitates more sophisticated and adaptive management of spectrum resources. Moreover, we propose a deep reinforcement learning (DRL) framework incorporating a modified proximal policy optimization (MPPO) algorithm. This algorithm is designed for better stability and convergence, outperforming the standard PPO in terms of faster convergence in the present URLLC transmission latency minimization process. Simulation results indicate that our proposed DT-based spectrum management and MPPO in CRNs result in a 27.89% increase in CRN's average throughput and a 39.94% reduction in transmission latency compared to the conventional equal resource allocation scheme.

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system’s training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions.

This paper explores simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink communications, focusing on maximizing information secrecy despite channel state information (CSI) errors. Perfect CSI is hard to achieve due to limited radio frequency chains at the STAR-RIS, noise, interference, and non-collaborative eavesdroppers. The study addresses the worst-case robust beamforming design problem to maximize the sum secrecy rate, considering transmit power limits, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. The S-procedure is used to estimate semi-infinite inequality constraints, followed by alternating optimization with a line search to iteratively update the precoder and phase shift matrix. Simulation results highlight STAR-RIS’s superior secrecy performance over conventional RIS and the algorithm’s efficiency across various scenarios.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes.

In this work, we explore the use of active reconfigurable intelligent surfaces (A-RIS) to improve vehicle-to-everything (V2X) communication systems to address limitations in traditional vehicular communication. In particular, we formulate an optimization problem to maximize the uplink sum rate for vehicle-to-infrastructure (V2I) links by optimizing transmit precoders, phase-shift matrices, transmit power, and spectrum sharing for vehicle-to-vehicle (V2V) links. To handle complex hybrid control scenarios, we propose a mixed-action deep reinforcement learning (DRL) algorithm and compare it with conventional benchmark methods like deep deterministic policy gradient (DDPG) with discrete actions (DA) and alternating optimization (AO). We evaluate the proposed algorithm’s effectiveness under imperfect channel state information as well. Simulation results highlight the efficacy of our approach, demonstrating significant enhancement in vehicular communication quality through A-RIS. Furthermore, we illustrate the impact of various factors such as number of A-RIS elements, vehicle speed, loss, execution time, amplification power, and CSI error on the performance of the V2X system.

In this work, we investigate the performance of an active reconfigurable intelligent surface (RIS)-aided multi-user uplink secure private industrial network. With an aim to provide a more sophisticated and consolidated framework towards the robust transmission design, we formulate a sum secrecy rate maximization while ensuring a minimum performance at each user within available resource constraints considering the norm-bounded imperfect channel state information (CSI) at Eavesdropper (Eve). To tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the equalizer, beamforming at the RIS, and power allocation at each user. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of active RIS, compared to passive RIS, towards a robust uplink secure private network is demonstrated. Finally, we discuss the impact of key parameters such as maximum power budget at each user and RIS, and CSI error on the secrecy performance of the considered network.

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

With the imminent arrival of 6G communication, the relevance of advanced technologies, such as multi-input-multioutput (MIMO), nonorthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC), has become prominent for plethora of Internet of Things (IoT) applications. However, integrating ISAC into a MIMO heterogeneous network (HetNets) necessitates reevaluating network performance in terms of outage probability and ergodic rates. This article introduces a novel analytical framework for evaluating downlink transmissions in MIMO HetNets. The proposed framework considers independent homogeneous Poisson point processes (PPP) for spatial arrangement of the NOMA-enabled base stations (BSs) and users. BS in the tth tier exploits superimposed NOMA signal for target sensing. Active RISs are considered to be distributed with homogeneous PPP and are used to mitigate blockage for user equipments when the direct link from the BSs does not exist. The approximated and asymptotic outage probability expressions are derived for two distinct scenarios: one involving direct transmission from the BS to the typical blocked user and the other entailing transmission via active RIS. Moreover, a practical case of imperfect successive interference cancelation (i-SIC) is considered. The analysis emphasizes the benefits of the proposed active RIS-NOMA compared to conventional orthogonal multiple access HetNets, and valuable insights are drawn by varying the number of RIS elements. Additionally, an increase in the RIS elements significantly improves the proposed active RIS-NOMA outage performance. The approximated expressions of ergodic rates, system throughput and beampattern for the sensing performance are also derived.

In this article, we propose a novel framework that combines simultaneous localization and communication (SLAC) using a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) systems. Our primary focus is on enhancing resource efficiency in such systems. We introduce Cloud Radio Access Networks (C-RAN) that facilitate collaboration between multiple base stations (BSs), enhancing cooperation benefits for both communication and sensing capabilities. To evaluate localization performance, we formulate an optimization problem to minimize the squared position error bound (SPEB) that reflects the system functional performance by optimizing the transmit beamformer, phase shift and subcarrier assignment under certain constraints. Moreover, in order to adjust the phase shift of the RIS, we propose a RIS-aided cooperative ISAC SLAC protocol. This approach utilizes the measurements collected to refine the location and velocity estimates of the agent, as well as to reconstruct the environmental map with enhanced accuracy. However, the high dimensionality of the decision space makes the problem computationally intensive and challenging to navigate using gradient-based or exhaustive search methods. To efficiently tackle these issues, we construct a framework based on Markov decision processes (MDPs) and address it by introducing a novel algorithm called hybrid deep reinforcement learning (HDRL) algorithm. We validate our proposed algorithm through various simulations, demonstrating its effectiveness in improving system performance by comparing with the baseline schemes.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided internet of things (IoT) communications under imperfect channel state information (CSI). We formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature of the above optimization problem, the MOOP is simplified using the.-constraint method and subsequently adopting advanced optimization tools, such as Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum HP on the performance of the considered system.

Vehicular crowdsensing (VCS) within the social Internet of Vehicles (IoV) significantly advances urban transportation management by enhancing road safety, traffic efficiency, and the overall driving experience. This article presents an intelligent multiagent deep reinforcement learning (DRL) framework for augmented dynamic task prioritization in a multi-incentive VCS system. Our framework, named intelligent multiagent reinforcement learning (IMARL), leverages augmented intelligence to integrate human-like decision-making processes with autonomous vehicle operations, ensuring more adaptive and robust task management. The proposed IMARL framework offers several key advantages: it dynamically adjusts the sensing levels of each vehicle, ensuring efficient energy usage and minimized processing times, and employs a data-quality aware multi-incentive utility model to capture both functional and social incentives. Additionally, our framework incorporates a layered server architecture, enhancing system resilience and scalability. Simulation results demonstrate the superiority of our approach. IMARL achieves significant improvements in task completion rates, energy consumption, and processing delays compared to other DRL and non-DRL benchmark methods. Furthermore, our approach exhibits strong adaptability to changing environmental conditions, maintaining high performance even in high-density traffic scenarios. These quantified results validate the effectiveness of the proposed framework, highlighting its potential to significantly enhance VCS systems in real-world applications.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO)-based approach for the DRL-based decision making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, nonuniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that the mPPO outperforms the conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential as a viable solution for real-world applications.

Next-generation wireless applications are expected to enable extended ultra-reliable low-latency communication (URLLC) to support high data rates along with ultra-reliability and low-latency features beyond the capabilities of existing core services. There is a need to transition from conventional architectures to more efficient and robust multiple-access schemes to meet these consolidated requirements in resource-constrained systems. This study explores the utilization of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) systems to enable spectrally efficient URLLC, even under the imperfect channel state information. In particular, we focus on maximizing spectral efficiency by jointly designing robust beamforming at the base station and STAR-RIS subject to given URLLC requirements. Due to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively by exploiting$\mathcal {S}-$procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode provides better spectral efficiency than other modes owing to its better interference management.

This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time.

As a revolutionary paradigm for green ultra-reliable low-latency communication (URLLC), reconfigurable intelligent surfaces (RISs) have been considered as a prominent architecture for enabling next-generation communication systems. Recently, a novel RIS framework, called simultaneous transmitting and reflecting (STAR-RIS), has been proposed to facilitate both transmission and reflection through the meta-material surface, leading to full-space coverage and even better beamforming flexibility than conventional RIS. This paper investigates an energy-efficient resource allocation design scheme for a STAR-RIS-aided downlink system under various STAR-RIS modes to deliver energy-efficient URLLC services by jointly optimizing the beamforming at the base station (BS) and STAR-RIS, subject to the given requirements on the rate, packet-error probability, and latency. Owing to the non-convex and NP-hard nature of the formulated problem, we propose an alternating optimization framework that obtains suboptimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively, in an iterative manner by exploiting fractional programming and successive convex approximation approaches. Simulation results confirm that the TS, ES, and MS modes of STAR-RIS achieve approximately$30\%-50\%$,$20\%-40\%$, and$10\%-15\%$, respectively better performance than a conventional reflecting-only RIS while guaranteeing strict reliability and latency requirements of URLLC. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode renders an effective solution due to its better interference management.

Ambient backscatter communication (AmBC) has emerged as a paradigm distinguished by its energy-efficient attributes and low-power dynamics, ideally suited to address the vast expanse of the Internet of Things (IoT). Unmanned aerial vehicles (UAVs) deployed with flexibility can effectively establish wireless connections for isolated IoT devices through AmBC. This paper delves into the exploration of secure transmission within a UAV-assisted AmBC network, particularly addressing the challenges posed by the presence of a passive eavesdropper. Specifically, a UAV is utilized as an aerial base station to offer services to an isolated ground user, an AmBC tag transmits its information to its associated receivers by leveraging the UAV’s radio frequency (RF) signals. Furthermore, a multi-antenna cooperative jammer is integrated within the system to intentionally interfere with the eavesdropper without affecting legitimate receivers. To characterize the secrecy performance, the expressions of secrecy outage probability of the air-ground link and backscatter link are both deduced leveraging a two-layer Gaussian-Chebyshev quadrature. Moreover, the asymptotic behaviors under the high signal-to-noise ratio (SNR) regime are also analyzed. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results.

This paper investigates an unconventional superposition scheme, i.e., rate-splitting multiple access (RSMA) to maximize the overall user fairness and high system performance gain for ultra-reliable low-latency communication (URLLC), enhanced mobile-broadband (eMBB) traffic coexistence in uplink scenarios. In particular, we focus on maximizing the worst-case performance of uplink eMBB and URLLC users when multiplexed in a given resource block using an effective rate-splitting approach among multiple sub-messages. Subsequently, a multi-objective optimization problem (MOOP) is formulated to jointly maximize the worst-case rate and minimize the worst-case packet-error probability (PEP) for eMBB and URLLC users, respectively, using effective power splitting and successive interference cancellation (SIC) decoding of the sub-messages. To solve the non-convexity of the formulated MOOP, we adopt a priori articulation scheme combined with the weighted product approach to transforming the MOOP into a single objective optimization problem (SOOP) and later, solve it using a low complex differential evolution (DE)-based meta-heuristic algorithm. We derive an optimal decoding strategy for sub-messages to ensure better user fairness among eMBB-URLLC traffic. Numerical simulations demonstrate the superiority of the considered RSMA-based superposition for hybrid eMBB-URLLC traffic over conventional slicing and superposition techniques. Moreover, the adopted weighted product method-based DE algorithm outperforms the state-of-art solutions.

Re-configurable intelligent surface (RIS)-aided communication has been envisaged as a frontier scheme to enable ultra-high spectral efficiency (SE) and energy efficiency (EE) for next-generation communication. This paper investigates an unconventional framework of RIS-aided full-duplex (FD) multi-user multiple-input multiple-output (MIMO) communication and analyzes its resource efficiency (RE), a preferable performance metric for realizing trade-off between SE and EE maximization. In particular, we focus on the RE maximization problem via a joint optimization of transmit covariance, optimal receive covariance, and phase-shift matrices for each RIS subject to the given constraint on the power budget. To solve the formulated non-convex problem, we propose two optimization approaches: a) policy gradient-based deep-reinforcement learning (DRL) algorithm based on a Markov decision process formulation for a stochastic-time varying channel and b) alternate optimization (AO) algorithm based on general approximations and majorization-minimization (MM) for static channel conditions. Simulation results validate the out-performance of the considered RIS-aided FD-MIMO system compared to the counterpart system with half-duplex (HD) mode and without RIS case. The proposed DRL algorithm achieves comparable RE performance with reduced computational complexity and running time compared to the traditional AO-based algorithm.

The substantial power consumption attributed to the active components within fully-connected reconfigurable intelligent surface (RIS) architecture significantly hinders the efficiency and sustainability of DT-enabled MEC networks. To tackle this challenge, we present an innovative sub-connected architecture for active RIS within the digital twin (DT) integrated mobile edge computing (MEC) framework of an Internet-of-Things (IoT) networks, capitalizing on edge intelligence to enhance ultra-reliable and low-latency communication (URLLC) services. The primary aim of our research is to improve uplink data transmission from IoT URLLC user nodes (UNs) to a base station (BS) with the aid of an active RIS, even under an imperfect channel state information (CSI). We have formulated the total end-to-end (e2e) latency minimization problem, which is solved by using an efficient alternating optimization (AO) algorithm. The algorithm breaks down the proposed non-convex problem into five subproblems, namely, beamforming design, caching and offloading policy optimization, joint communication and computation optimization, and joint active RIS phase shift and amplification factor vector optimization. We conducted a thorough analysis of the convergence properties of the proposed AO algorithm, benchmarking its performance against the established Heuristic algorithm. Our simulation results consistently demonstrate the superiority of our proposed DT-assisted optimal phase sub-connected active RIS scheme over various benchmark schemes, taking into account various factors such as the number of RIS elements, power budget constraints, imperfect CSI, edge computing server (ECS) cache capacity, number of IoT UNs, and the number of power amplifiers.

This paper explores the state-of-the-art terahertz (THz) integrated sensing and communication system (ISAC) that uses active reconfigurable intelligent surfaces (ASRIS) that can transmit and reflect signals at the same time. The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicular units (VUs), and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system is equipped with a dual-function radar and communication multiple-antenna base station (BS), which simultaneously serves both communication and target sensing functions through an ASRIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, ASRIS reflection and transmission beamforming matrices, VU mobility correlation parameters, and radar receive filter. Traditional optimization methods prove challenging given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables. To overcome this, we propose a machine learning (ML)-based multi-agent deep deterministic policy gradient (MADDPG) algorithm. MADDPG enables collaborative learning, adapts to the dynamic communication environment, and excels in optimizing interdependent parameters in the proposed THz system. Deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and modified-PPO (MPPO) algorithms serve as benchmarks, showcasing the distinctive advantages of the ML-based MADDPG solution for the proposed system’s complexities. Our simulations validate the substantial benefits of ASRIS over conventional RIS benchmark scenarios.

In this study, we investigate the performance of a hybrid reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) network, augmented with backscattering capabilities, designed to facilitate integrated sensing and communication (ISAC). Our primary objective is two-fold: first, to enhance the overall communication throughput, and second, to strengthen the sensing power for target detection. To achieve these goals, we introduce two novel dynamic user clustering algorithms namely composite distance and angle-based (CDA) and channel-oriented adaptive (COA) algorithm for grouping users into clusters with fixed base station and RIS positions, where the successive interference cancellation (SIC) is employed for effective communication within each pair. Moreover, we present a comprehensive optimization problem that jointly maximizes the sum rate and sensing power. This problem involves optimizing the transmit beamformer at the base station, the power allocation factors within each cluster, and the phase shifts at the RIS. This methodology not only adheres to strict power constraints and quality of service requirements at each receiving node but also ensures equitable resource allocation among the targets and enforces unit modulus phase shifts at each RIS element. To tackle the complex interdependencies and non-convex nature of the optimization problem, we introduce an advanced iterative algorithm based on alternative optimization (AO). This state-of-the-art technique employs successive convex approximation (SCA) to systematically address this multifaceted problem. Finally, the simulation results empirically validate the proposed algorithm’s effectiveness, considering the number of RIS elements, maximum power budget, number of targets, and imperfect channel state information (CSI) while showing the trade-off between communication and sensing performance.

Next-generation wireless applications are expected to enable extended ultra-reliable low latency communication (xURLLC) to support high data rates along with ultra-high reliability and low end-to-end latency features beyond the capabilities of existing core services. These consolidated data rates and URLLC requirements in resource-constrained systems necessitate the shift from conventional architectures to more powerful and robust multiple access schemes. This paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted rate-splitting multiple access (RSMA) to prompt an unconventional xURLLC service called mobile broadband reliable low latency communication (mBRLLC) for high spectral efficiency under finite block-length (FBL) transmission constraints. To enable spectral-efficient resource allocation, we formulate a sum throughput maximization problem for joint optimization of precoder design at the base-station (BS), block-length of common and private symbols of each user, and passive beamforming at each RIS. To solve the NP-hardness and non-convexity of the formulated problem, we use an alternating optimization technique to decouple the original problem into three sub-problems: active beamforming at the BS, block-length optimization, and passive beamforming at each RIS which are solved using general convex approximations. Simulations demonstrate the effectiveness of the proposed resource allocation algorithm over conventional schemes. The considered RSMA system achieves high data rates even with lower latency and higher reliability. Additionally, the investigation encompasses the evaluation of RIS deployment implications, the analysis of the worst-case latency scenario, and the assessment of the influence of channel estimation errors.

The low-resolution reality of the hardware elements associated with massive mmWave antenna or reflector arrays is associated with the performance degradation of the wireless link when it is not properly controlled. In particular, the unintended angular radiations of the transmission or reflection arrays (e.g., transmission in non-intended directions) would invalidate the usual assumptions of information secrecy, even with perfect channel state information (CSI) knowledge at the transmitter, in the presence of low-resolution hardware. In this paper, we study a hybrid beamforming design for reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) communication, from the prospect of information secrecy maximization, wherein the array element phase rotations belong to the known discrete space. To address the NP-hard and non-convex nature of the problem at hand, we propose an iterative procedure by re-structuring the obtained discrete-domain problem into a tractable form which solves the problem numerically and guarantees the convergence to a stationary point. Further, we confirm the accuracy of the proposed optimization algorithm by an exhaustive search method based on graphical simulations. The minimal performance disparity that exists between the proposed algorithm and the considered digital beamforming (DBF) scheme as the upper bound validates the hybrid beamforming design. Moreover, the proposed work highlights the superiority of discrete-aware design over various existing baseline schemes, demonstrating the significant gains attainable by adopting discrete space design from the outset. Additionally, the proposed solution discusses the improvement in secrecy system performance by deploying RIS with an increased number of reflecting elements and thereby restricting the effect of eavesdroppers on secure communication.

This work proposes a new model of reconfigurable intelligent surface (RIS) called cognizable RIS (CRIS) that is specifically designed to meet the unique demands of users who require extreme-ultra-reliable and low-latency Communication (xURLLC) in the sixth generation (6G) wireless networks. The programmable elements in the proposed CRIS unit can adapt to different modes of operation to provide significant performance gain. To improve reliability at the receiver, we integrate unmanned aerial vehicles with the CRIS module, which enhances network performance through beamforming and mobility. Our study focuses on maximizing the sum throughput in a multiple-input multiple-output scenario using the rate-splitting multiple access communication system. To achieve this, we introduce a novel hybridized multi-agent-based deep reinforcement learning (DRL) algorithm for optimal resource allocation that maximizes the sum throughput. We incorporate long-short-term memory (LSTM) networks into our proposed DRL to address the temporal dependencies due to stochastic channel conditions. By utilizing the proposed LSTM-based multi-agent DRL (MA-DRL) algorithm, we achieve notable gains of 11.7% and 26.9% in sum throughput over widely recognized DRL benchmark algorithms, all while adhering to xURLLC’s stringent maximum packet error probability constraint of 10−9.

As a revolutionary paradigm for green communication architecture for next-generation, reconfigurable intelligent surfaces (RISs) has been considered for simultaneous wireless information and power transfer (SWIPT). Nevertheless, the performance gain achieved by the conventional passive RISs is limited due to the multiplicative fading effect. In this paper, we investigate an unconventional framework of active reconfigurable intelligent surface (ARIS) aided multi-user (MU) multi-input multi-output (MIMO) system to captivate better performance for the SWIPT system. Particularly, we focus on the problem of power minimization via joint beamforming design at the base station (BS) and the ARIS for the considered SWIPT system under statistical channel estimation error (CEE) while guaranteeing the minimum rate requirement and the minimum energy-harvested constraints for information and energy receivers, respectively. Owing to the non-convex and NP-hard nature of the formulated problem, we first utilize a minimum mean square error (MMSE) approach to transform the problem into its simplified form, and later utilize an alternating optimization framework which solves the problems of beamforming design at the BS and the ARIS independently in an iterative manner using general approximations. Simulation results confirm that the ARIS can significantly reduce the required transmission power by 50-60% when compared to passive RIS while satisfying given QoS constraints for SWIPT system under the CEE model.

In this paper, we propose a novel approach for optimal resource management in ultra-reliable low-latency communication (URLLC)-enabled Internet of Vehicles (IoV) networks. The framework includes mobile edge computing (MEC) servers integrated into roadside units (RSUs), unmanned aerial vehicles (UAVs), and base stations (BSs) for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. We utilize asynchronous federated learning (AFL) approach to enhance the accuracy of the global model by considering the mobility characteristics of vehicles. The problem of optimal resource allocation is formulated to achieve the best allocation of frequency, computation, and caching resources while complying with the delay restrictions. To solve the non-convex problem, a multi-agent actor-critic type deep reinforcement learning algorithm called D-MAAC algorithm is introduced. Extensive simulations show the effectiveness of the proposed framework and algorithms compared to existing schemes.

Next-generation wireless applications are expected to enable extended ultra-reliable low latency communication (URLLC) to support high data rates along with ultra-high reliability and low end-to-end latency features beyond the capabilities of existing core services. This paper investigates simultaneous transmission and reflection re-configurable intelligent surface (STARRIS) aided non-orthogonal multiple access (NOMA) systems to enable spectral-efficient short-packet communication under imperfect channel state information. In particular, we focus on the problem of spectral-efficiency maximization via joint beamforming design at the base station and STAR-RIS subject to given URLLC requirements. Owing to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the robust beamforming design, respectively by exploiting$S$-procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements.

This work proposes a novel design for an active reconfigurable intelligent surface (RIS)-assisted digital twin (DT) based mobile edge computing (MEC) model that leverages edge intelligence to enhance ultra-reliable and low-latency communications (URLLC) services in Internet-of- Things (loT) networks. The system model considers uplink data transmission from the single antenna IoT-URLLC nodes (UNs) to a multi-antenna base station (BS) with the aid of an active RIS under imperfect channel state information (CSI). We formulate a total end-to-end (E2E) latency minimization problem for the proposed system model. An efficient alternating optimization (AO) algorithm is proposed to tackle the non-convexity of the problem by reformulating it into five subproblems: beamforming design, caching and offloading policies optimization, joint communication and computation optimization, and active RIS phase shift optimization. Simulation results demonstrate that the proposed DT-assisted optimal-phase active RIS scheme consistently outperforms benchmark schemes, such as optimal-phase passive RIS, random-phase active RIS, and no- RIS systems, considering factors such as imperfect CSI, power budget, number of RIS elements, the caching capacity of edge computing server (ECS) and the number of loT UNs.

In this paper, we consider the digital twin (DT) of a reconfigurable intelligent surface (RIS) aided communication system, whereby the problem of mitigating the effects of increasing hardware impairments at the RIS system to increase its lifespan is studied. A base station (BS) with dedicated control signals inspects a hardware-impaired RIS system in real time. The DT determines whether systematic maintenance should be done immediately or postponed in order to increase the expected lifespan of the RIS system. To make an optimal decision, the DT uses information from the BS about the level of impairment to effectively switch between the RIS elements. Furthermore, we focus on the case of single systematic maintenance with a deterministic failure threshold but also discuss generalizations for multiple systematic maintenances. Extensive numerical examples are provided to elucidate the optimal solutions obtained and illustrate the efficacy of the proposed method.

The proliferation of multiple devices and acceleration of spectral efficiency has become a pivotal requirement for the unprecedented connectivity and performance of vehicle-to-everything (V2X) networks. This paper investigates an unconventional framework of reconfigurable intelligent surface (RIS)-integrated full-duplex (FD) rate-splitting multiple access (RSMA) communication systems, which aims to maximize the spectral efficiency of uplink (UL) and downlink (DL) vehicles in V2X network. In particular, a robust spectral-efficient design for the considered RIS-integrated FD-RSMA system via joint beamforming design and power allocation at UL vehicles under imperfect channel state information is investigated. To tackle the non-convexity of the original sum-rate maximization problem, we adopt a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) algorithm which leverages Markov decision process formulation. Simulation results demonstrate the effectiveness of the integration of RIS, RSMA, and FD schemes for V2X networks over half-duplex (HD) and multi-user linear precoding schemes. Furthermore, the superiority of the proposed PPO algorithm is validated over the counterpart deep deterministic policy gradient algorithm (DDPG).

This paper investigates the problem of spectral efficiency maximization in an underlay cognitive radio network (CRN) utilizing rate-splitting multiple access (RSMA) transmission for MISO downlink under short packet communications and imperfect channel estimation information. In particular, we focus on an effective transmit beamforming design at the cognitive base station while satisfying the requirements of ultra-reliable and low-latency communication (URLLC), interference temperature, power budget, and minimum throughput. We model the dynamic resource allocation problem as a Markov decision process (MDP) and employ deep reinforcement learning techniques, specifically the deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) algorithms, while taking into account the time-varying channel conditions. Simulation results demonstrate that the DDPG algorithm outperforms PPO at low interference temperatures for the primary receiver, while the opposite holds at high interference temperatures. Moreover, the considered RSMA system for CRN outperforms traditional multi-user linear precoding and power-domain multiple access schemes while maintaining small packet sizes and high reliability.

In this work, we consider a full-duplex (FD) communication system that uses a simultaneous transmission and reflection (STAR) enabled reconfigurable intelligent surfaces (RIS) to assist the communication and sensing between a base station (BS) to a single set of UL and DL user, and target over the same-time frequency dimension. In order to explore the performance of the proposed framework, we offer an analytical framework and accordingly, we propose an optimization problem to jointly optimize the phase-shift matrices at the STAR RIS (S-RIS) that maximizes the possible sum-rate. Due to the non-convexity of the optimization problem, we then propose a low-complexity meta-reinforcement learning (MRL) algorithm that reduces the overall training overhead. We also demonstrate the effectiveness of the proposed algorithm in providing near-optimal design in the case of imperfect channel state information (ICSI). Additionally, in order to verify how well the proposed framework work and to show the superiority of the proposed algorithm, we provide a fair comparison with two baseline schemes a) twin delayed deep deterministic policy gradient (TD3) and b) deep deterministic policy gradient (DDPG). Simulation results verify that the proposed approach results in superior performance.

In this paper, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted integrated sensing and communication system (ISAC), where S-RIS is enabled to broadcast communication signal, and P-RIS assists sensing functionalities. In particular, we jointly optimize the beamforming vector at the multi-antenna ISAC transmitter, and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit at ISAC transmitter while ensuring the performance of sensing model to detect targets in its vicinity and limitations of phase and amplitude of S-RIS elements. To address the non-convexity of the above problem, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide a comprehensive simulation-based graphical results to verify the viability of the proposed framework with its P-RIS assisted counterpart. Eventually, exhaustive simulation results are demonstrated to present the impact of RIS elements and the number of antennas at the ISAC transmitter. Accordingly, we illustrate the impact of S-RIS and the number of targets to highlight the trade-off between sensing and communication.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided green multiple input single output (MISO) communications under imperfect channel state information (CSI). With an aim to provide green and robust transmission, we formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that maximizes the energy efficiency at the information receivers and harvested power at the energy receivers under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature, the MOOP is simplified using the$\epsilon$-constraint method. We then propose an alternating optimization-based iterative algorithm that utilizes semidefinite programming to determine the optimal TPV and PSM one by one until convergence is achieved. Via exhaustive simulations, we graphically demonstrate the effectiveness and robustness of the proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum harvested power at each energy receiver on the performance of the considered system.

In this paper, we explore a novel approach of using a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to aid a multi-user (MU) multi-input multi-output (MIMO) system for simultaneous wireless information and power transfer (SWIPT) in the presence of statistical channel estimation errors (CEE). Our focus is on minimizing the power required for the SWIPT system through joint beamforming design at both the base station (BS) and STAR-RIS, while ensuring that the minimum rate and minimum energy harvesting requirements are met for information and energy receivers, respectively. Due to the non-convex and NP-hard nature of the problem, we utilize a minimum mean square error (MMSE) approach to simplify the problem and then use an alternating optimization framework to solve the beamforming design problems at the BS and STAR-RIS iteratively using general approximations. Simulation results show that the proposed algorithm provides a significant beamforming gain for STAR-RIS-aided SWIPT system over conventional RIS system while satisfying the given quality of service (QoS) constraints for SWIPT systems under the CEE.

The cell-free massive multiple-input and multiple-output (CF-mMIMO) communication technology has the ability to handle inter-cell interference in MIMO systems, making it a potential candidate for sixth-generation (6G) wireless communication. A CF-mMIMO system is investigated in this paper for mission-critical ultra-reliable low latency communication (URLLC) applications involving a central processing unit (CPU), many distributed access points (APs), each with multiple antennas, and multiple single-antenna user equipment (UEs). In order to maximize energy efficiency (EE) and throughput gains, each AP is linked to the CPU through a fronthaul link with limited capacity, which handles the quantized uplink data to the CPU. We assume that each AP serves fewer UEs. Our approach has a minimal signal processing complexity and offers UEs uniform quality of service (QoS) as well as improved EE. Closed-form expression for outage probability (OP) in the uplink of the CF-mMIMO system considering Welch-Satterthwaite approximation is derived using a variety of Doppler power spectra (DPS) models that consider imperfect channel state information (CSI) and mobility of UEs. Our numerical simulations validate the correctness of the derived expressions.

This paper investigates an unconventional framework of active reconfigurable intelligent surface (ARIS) aided multi-user (MU) multi-input multi-output (MIMO) system for simultaneous wireless information and power transfer (SWIPT) network under statistical channel estimation error (CEE). Particularly, we focus on the problem of power minimization via joint beamforming design at BS and ARIS for considered SWIPT system while guaranteeing the minimum rate requirement and the minimum energy-harvested constraints for information and energy receivers, respectively. Owing to the non-convex and NP-hard nature of the formulated problem, we first utilize a minimum mean square error (MMSE) approach to transform the problem into its simplified form, and later utilize an alternating optimization framework which solves the problems of beamforming design at BS and ARIS independently in an iterative manner using general approximations. Simulation results confirm that ARIS can significantly reduce the required transmission power by 50-60% when compared to passive RIS while satisfying given QoS constraints for SWIPT systems under the CEE model.

This paper investigates an unconventional framework of rate-splitting multiple access (RSMA)-integrated full-duplex (FD) system to attain spectral-efficient multi-user communication. The considered FD-RSMA system divides and encodes the original messages of each downlink (DL) and uplink (UL) into two different sub-messages, and later transmits them at the same resource block, resulting in strong inter-user interference and cross-link interference, i.e., self-interference (SI) and co-channel interference (CCI). Specifically, we focus on maximizing the sum rate of the considered FD-RSMA system via joint power allocation for simultaneous UL and DL communication, subject to transmit power constraints and given quality of service (QoS) requirements. To tackle the non-convexity of the formulated problem, we adopt an iterative algorithm that employs semidefnite programming (SDP), majorization minimization (MM), and inner approximation (IA) techniques to attain near-optimal resource allocation with effective interference management. Simulation results validate that the FD-RSMA scheme outperforms conventional half-duplex, multi-user linear precoding, and non-orthogonal multiple access schemes.

In order to support the need for higher spectral and energy efficiencies with a wider coverage area, simultaneous refracting/transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and rate splitting multiple access (RSMA) have emerged as the potential technologies required for architectural advancement in the next-generation wireless communication networks. In this work, we propose a novel analytical framework of an RSMA-enhanced STAR-RIS-aided downlink multi-user communication system. First, we discuss the statistical characteristics of the different channels involved in the transmission and derive their probability density function (PDF). Using the derived PDF, we analyze the performance of the system and derive the analytical closed-form expressions of the outage probability at each reflecting and refracting downlink user for two different STAR-RIS operational protocols namely i) energy splitting (ES) and ii) mode switching (MS). Furthermore, we validate the accurateness of the all analytical expressions through Monte-Carlo (MC) simulations. We also highlight the impact of some important parameters of the system such as transmit power at the BS, elements in the STAR-RIS, imperfect channel state information (CSI) on the outage probability of each user. Finally, we demonstrate the dominance of RSMA over non-orthogonal multiple access (NOMA) on the system performance.

In this paper, a new instantaneous channel state information-based dynamic time-switching (TS) energy harvesting (EH) protocol is proposed for a two-hop battery-assisted full-duplex (FD) relay network. To ensure reliable communication, a quantum of the battery energy augments the harvested energy. Assuming nonlinear EH, performance is evaluated in terms of throughput for static TS and dynamic TS protocols. In such networks, node-level energy considerations are clearly important but have not attracted research attention. It is demonstrated that by jointly optimizing the average battery energy consumption and the TS parameter, the throughput for both static and dynamic TS protocols can be maximized. It is also demonstrated that the proposed dynamic TS protocol provides substantial gains in throughput and average energy savings compared to the static TS protocol. The accuracy of the derived analytical expressions is verified through Monte Carlo simulations.

Simultaneous refracting/transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has emerged as a potential technology for future-generation wireless networks to support extremely high data rates with a broader coverage area. In this work, with an aim to provide a novel analytical framework, we investigate the performance of a STAR-RIS assisted full duplex (FD) wireless communication system under finite block length (FBL) transmission. In particular, we first derive the probability density function and cumulative distribution function of the signal-to-interference-plus-noise ratio (SINR) for the uplink and downlink users. We then analyze the system performance by deriving closed form expressions for their block error rate (BLER) and goodput. Finally, we validate the accuracy of the derived analytical expressions using Monte-Carlo simulations and show that as the number of elements in the STAR-RIS is increased the system performance also improves. Furthermore, we graphically demonstrate the impact of imperfect channel state information and compare the performance of STAR-RIS in mode switching (MS) and energy splitting (ES) protocol.

Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.

In this work, we consider a novel reconfigurable intelligent surface (RIS)-assisted full duplex (FD) sixth generation (6G)-vehicle-to-everything (V2X) communication network having a FD base station (BS) simultaneously communicating with an uplink (UL) and a downlink (DL) mobile vehicles with the aide of two RISs, one for each link. We provide an analytical framework to investigate the performance of this network and, consequently, formulate an optimization problem to jointly optimize the phase-shift matrices at both the RISs that maximizes the achievable sum-rate. Thereafter, we propose a successive refinement algorithm which uses an iterative approach to solve the problem and provide optimum values of phase-shift matrix at each RIS. We validate the accuracy of the proposed algorithm by exhaustive simulation based graphical results. Accordingly, we demonstrate the dominance of the considered FD system over its half-duplex (HD) counterpart. Moreover, we also highlight the impact of imperfect self interference cancellation and discuss the trade-off between the UL and DL performances due to this imperfection.

In this work, we investigate the performance of a federated edge learning (FEEL)-enhanced edge computing in unmanned aerial vehicles (UAV)-aided internet of things (IoT) system under the consideration of limited energy at each UAV. It consists of multiple UAVs which apply FEEL for local training and, then, transmit the required parameters to a centralized IoT-server. We use a new cost metric obtained by a linear combination of latency and energy consumption, and formulate an optimization problem to jointly optimize the central processing unit (CPU)-frequency during FEEL and allotted bandwidth under the consideration of the limited overall system bandwidth and energy available at each UAV. Due to the non-convex nature of the formulated problem, we propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm that solves the problem and provides the optimum CPU frequency and allotted bandwidth to each user. We validate the accuracy and convergence of the proposed algorithm via exhaustive simulations and highlight its effectiveness by comparing its performance with that of deep deterministic policy gradient (DDPG) and deep Qnetwork (DQN)-based solutions.

Digital twin (DT) has emerged as a promising technology for improving resource allocation decisions in Internet of Vehicles (IoV) networks. In this paper, we consider an IoV network where mobile edge computing (MEC) servers are deployed at the roadside units (RSUs). The IoV network provides ubiquitous connections even in areas uncovered by RSUs with the assistance of unmanned aerial vehicles (UAVs) which can act as a relay between RSUs and task vehicles. A virtual representation of the IoV network is established in the aerial network as DT which captures the dynamics of the entities of the physical network in real-time in order to perform efficient resource allocation for delay-intolerant tasks. We investigate an intelligent delay-sensitive task offloading scheme for the dynamic vehicular environment which provides computation resources via local execution, vehicle-to-vehicle (V2V), and vehicle-to-roadside-unit (V2I) offloading modes based on the energy consumption of the system. Moreover, we also propose a multi-network deep reinforcement learning (DRL)-based resource allocation algorithm (RADiT) in the DT-assisted network for maximizing the utility of the IoV network while optimizing the task offloading strategy. Further, we compare the performance of the proposed algorithm with and without the presence of V2V computation mode. RADiT is further evaluated by comparing it with another benchmark DRL algorithm called soft actor-critic (SAC) and a non-DRL approach called greedy. Finally, simulations are performed to demonstrate that the utility of the proposed RADiT algorithm is higher under every condition compared to its respective conditions in SAC and greedy approach. Consequently, the proposed framework jointly improves energy efficiency and reduces the overall delay of the network. The proposed algorithm with UAV relay further increases the efficiency of the network by increasing the task completion rate.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

With the advancement of wireless communication technologies, reconfigurable intelligent surfaces (RISs) have recently paved the way to augmenting the performance of wireless networks with the aid of multiple reflecting surfaces by efficiently attuning the signal reflection through a large number of low-cost passive elements. In this paper, we consider an RIS-aided full-duplex (FD) communication network consisting of a FD access point (AP) that communicates with an uplink and a downlink user simultaneously with the aid of an RIS as well as through the direct link between the AP and users. To evaluate the system performance under infinite blocklength (IBL) and finite blocklength (FBL) codes, we derive the analytical expressions for the outage probability and throughput in case of IBL, and for block-error rate (BLER) and goodput in the case of FBL, for both uplink and downlink transmission. Furthermore, the expressions for the maximum achievable rate under FBL and IBL transmission are derived. Next, we also extend the analysis of the single-user framework to a more practical scenario with multiple users utilizing non-orthogonal multiple access (NOMA) and derive analytical expressions for the outage probability and BLER at each downlink user and at the AP. The accuracy of the derived expressions is validated via simulation results, and insights are provided regarding the impact of the number of reflecting elements and imperfect channel state information (CSI) on the performance of the considered system. Finally, from the comparative analysis, it is shown that the RIS-aided system outperforms the system without RIS in both IBL and FBL scenarios, providing remarkable improvement in the outage probability and BLER.

The efficient and effective framework for next-generation (5G and beyond 5G) wireless networks should include mission-critical aspects such as ultralow latency ($\leq \!\!1$ms), ultrahigh reliability (99.999%), and enhanced data rate. Billions of ubiquitously connected devices are expected to serve various industrial applications in upcoming industry standards such as Industry 5.0. These industrial applications include mission-critical tasks such as smart grids, remote surgery, and intelligent transportation systems. This article considers an industrial Internet of Things (IIoT) environment in mission-critical ultrareliable low latency communication (URLLC) application where the main industrial unit or industrial control node (CN) sends messages to the target device (TD) with the aid of a cooperative device (CD). We investigate a novel transmission protocol and analyze the network’s performance. Considering the nonlinear energy harvesting (EH) mechanism at power-constrained nodes and direct and cooperative phase transmissions, the outage probability (OP) and block error rate (BLER) performances are evaluated for Rayleigh distributed fading channels. The analytical results are validated through Monte–Carlo simulations.

In this paper, an intelligent reflecting surface (IRS) aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated for dead-zone users where the direct link between the users and the base station (BS) is unavailable and the UL transmission is carried out only through IRS. In the considered RSMA system, a message of each user is split into several sub-messages and each part contributes to the rate of that user and depending upon split proportions BS decodes them using appropriate decoding order. The problem of sum-rate maximization is formulated to jointly design the optimal power allocation at each UL user, passive beamforming at the IRS under optimal decoding order of sub-messages. Due to non-convexity and discrete non-linear programming of the formulated problem, the original problem is intractable and hence, we decouple the problem into different sub-problems in which the problems of power allocation and passive beamforming are alternatively solved under using successive convex approximation and Riemaniann conjugate gradient algorithms, respectively. Moreover, the decoding order strategy is analytically derived which confirm that the optimal decoding order strategy depend upon decreasing order of channel gain of users and increasing order of split proportions of sub-messages. Later, the unified solution based on block-coordinate descent (BCD) algorithm is proposed. Simulation results validate that the proposed decoding order scheme attains performance closer to the optimal solution with low computational complexity. Moreover, the proposed IRS aided RMSA system outperforms the system with non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes in terms of achievable sum-rate throughput.

In this paper, a phase-shift coupled simultaneous transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated to achieve improved spectral efficiency. The considered UL RSMA system splits the rate for each user by dividing their message into multiple sub-messages and these sub-messages are transmitted to the base station (BS) via STAR-RIS as direct link between BS and user is absent. In particular, we formulate a resource allocation design problem which aim to maximize the overall rate-throughput of the considered system under the joint optimization of power allocation, decoding order, user-fairness and beamforming design at STAR-RIS for various operating modes of STAR-RIS modes, which is mixed- integer non-linear programming (MINLP). To solve the formulated non-convex complex problem, we first transform the original sum-rate maximization into its simplified form and then solved it using an alternating optimization algorithm where the sub-problems of power allocation and beamforming design under given decoding order are solved alternatively using general convex approximation and fractional programming approaches. Numerical simulation and computational complexity analysis validate that the proposed solution attains fast convergence. Moreover, the proposed RSMA scheme in STAR-RIS aided UL system outperforms the conventional nonorthogonal multiple access and orthogonal multiple access schemes in terms of overall rate-throughput and user-fairness.

In this work, we investigate rate-splitting multiple access (RSMA) for a multiuser downlink wireless network consisting of an unmanned aerial vehicle (UAV)-assisted base station (BS) that serves multiple ground users (GUs) simultaneously. Considering two different transmission regimes, namely infinite blocklength (IBL), and finite blocklength (FBL), we analyze the performance of the considered network under the effect of imperfections in channel state information (CSI) estimation and successive interference cancellation (SIC) with the probabilistic line of sight fading channels. For IBL transmission, we derive the closed-form expressions of the outage probability, throughput, and achievable ergodic rate at each GU. Furthermore, for short packet communication, the closed-form expressions of block error rate (BLER), goodput, and achievable ergodic rate are determined with FBL transmission. Moreover, for the FBL regime we also formulate an optimization problem that jointly optimizes the 3D-position of the UAV, power allocated to each user, and common rate distribution at each user to maximize the ergodic sum rate subject to the practical constraints such as maximum tolerable BLER, minimum private and total rate at each user. We then propose an alternating optimization-based iterative algorithm to solve the problem. Monte-Carlo simulations are used to verify the accuracy of derived analytical results and demonstrate the trade-off between transmit power and achievable BLER. In addition to this, the effectiveness of RSMA in UAV-assisted communication under IBL and FBL transmission regimes is also observed compared to non-orthogonal multiple access.

In this work, we consider a hybrid aerial full-duplex (FD) relaying consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of rate splitting multiple access (RSMA) in such networks and focus on joint optimization of RSMA parameters, 3D-coordinates of the UAV/RIS, and phase shift matrix at the RIS along with analyzing the outage probability, block error rate (BLER) and achievable weighted sum rate for finite blocklength (FBL) and infinite blocklength (IBL) codes under imperfect successive interference cancellation (SIC) at each user and residual-self interference (RSI) at the UAV. We first formulate the weighted sum rate maximization problem and adopt the block coordinate descent (BCD) method to deal with the non-convex nature of the problem. Thereafter, we propose a BCD-based algorithm that jointly optimizes these parameters using a heuristic approach for optimum power allocation, a Riemannian conjugate gradient-based algorithm to get the optimal phase shift at the RIS, and an iterative algorithm to obtain the optimal UAV/RIS position. It also distributes the common rate among the users optimally. Next, with obtained optimal parameters, we further analyze the performance of the network and derive the closed-form expressions of BLER, outage probability, and average weighted sum rate. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions, and demonstrate the superiority of RSMA over non-orthogonal multiple access (NOMA) and conventional orthogonal multiple access (OMA) schemes.

Reconfigurable intelligent surface (RIS) is an emerging next-generation technology that can improve the energy efficiency using multiple low-power passive metasurfaces which reflect the desired signal to the users. In this work, we consider a RIS-assisted underlay multiuser multiple-input multiple-output cognitive radio network and formulate a weighted energy efficiency maximization problem in order to jointly optimize the active precoding matrix (APM) at the secondary transmitter and passive precoding matrix (PPM) at the RIS subject to the constraints of available transmission power at the secondary transmitter and maximum allowable interference towards the primary user/receiver. However, due to the coupling of APM and PPM variables, the problem becomes non-convex, and conventional optimization methods cannot be used to solve it. Therefore, by adopting the weighted minimum mean-square error method we first transform the non-convex objective function into a convex one. Next, based on the block coordinate descent method, we propose an iterative algorithm that determines the optimal APM and PPM using Lagrange dual decomposition method and inner approximation method, respectively. Finally, the optimality and efficacy of the proposed algorithm are validated using numerical simulations. The impact of the channel state information (CSI) error has been studied via numerical simulations on the proposed network and it shows that the proposed design is relatively robust against the CSI imperfections, especially at low SNR conditions.

The recent advent of simultaneous refracting and re-flecting reconfigurable intelligent surface (STAR-RIS) has paved the way for next generation wireless technology by enhancing the quality of the signal with wider coverage area connectivity. In this work, we propose a novel STAR-RIS assisted full duplex (FD) wireless communication system where a FD base station (BS) communicates with an uplink user and a downlink user simultaneously with the aid of a STAR-RIS. First, we derive the probability density function (PDF) of the uplink and downlink signal to interference plus noise ratio (SINR). Using the derived PDF, we analyze the system performance and derive analytical closed-form expressions for the outage probability and achievable throughput for both the uplink and downlink communication. Finally, we validate the accuracy of the derived analytical expressions using Monte-Carlo simulations and show that the use of the STAR- RIS provides a significantly improved performance compared to the conventional RIS.

This paper investigates a power-efficient simultaneous wireless information and power transfer (SWIPT)-enabled reconfigurable intelligent surface (RIS)-assisted multi-input multi-output (MIMO) communication network, where a multiple antenna base station (BS) communicates with multiple information and energy receivers simultaneously with the aid of a RIS. To jointly optimize the active and passive beamforming, a transmit power minimization problem is formulated subject to minimum rate and harvested energy requirements at respective users. Due to non-convex nature of the formulated problem, we adopt the mean squared error (MSE)-based approach to convert it into a convex one. Next, we propose an iterative algorithm to determine the optimal active and passive beamforming matrices. Finally, simulation results are presented to validate the effectiveness of the proposed algorithm and highlights the impact of important parameters such as reflecting elements, minimum rate constraint, etc. It is established that the use of RIS along with the proposed algorithm requires approximately 10%–15% less power compared to the algorithms with random phase shifts and no-RIS.

Reconfigurable Intelligent Surface (RIS) is a transformative technology which can enhance the performance of the ubiquitous wireless networks and achieve better signal quality with the aide of multiple reflecting surfaces. In this work, an analytical framework of a RIS-aided full duplex (FD) communication network consisting of a FD-access point (AP) that communicates with an uplink and a down-link users simultaneously is provided. In particular, we analyze the performance of the considered system by deriving analytical expressions of outage probability for both uplink and downlink transmissions. Further, the accuracy of the derived expressions is validated using simulation results. Finally, from the comparative analysis, it is shown that the RIS outperforms the system without RIS providing remarkable improvement in the outage probability.

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting.

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network.

In this work, we consider a hybrid aerial full-duplex (FD) relaying protocol consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in the decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of non-orthogonal multiple access (NOMA) in such networks and focus on both the performance analysis and design optimization of the considered RIS-NOMA network under imperfect channel state information (CSI) and successive interference cancellation (SIC) at each user, and residual-self interference (RSI) at UAV. We first formulate the sum rate maximization problem and adopt the block coordinate descent method to deal with the non-convex nature of the problem. Thereafter, we propose an algorithm based on the Riemannian conjugate gradient method to get the optimal phase shifts at the RIS, an iterative algorithm to obtain the optimal UAV/RIS position and the exhaustive method to obtain the optimum power allocation coefficients. Next, with obtained optimal position, phase shift and power coefficients, we further analyze the performance of the network and derive the closed-form expressions of outage probability, achievable throughput and ergodic capacity. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions and demonstrate the superiority of NOMA over OMA.

Due to the dynamic nature of a vehicular fog computing environment, efficient real-time resource allocation in an Internet of Vehicles (IoV) network without affecting the quality of service of any of the onboard vehicles can be challenging. This article proposes a priority-sensitive task offloading and resource allocation scheme in an IoV network, where vehicles periodically exchange beacon messages to inquire about available services and other important information necessary for making the offloading decisions. In the proposed methodology, the vehicles are stimulated to share their idle computation resources with the task vehicles, whereby a deep reinforcement learning algorithm based on soft actor–critic is designed to classify the tasks based on priority and computation size of each task for optimally allocating the power. Furthermore, we also design deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms for the considered framework. In particular, the algorithms work toward achieving the optimal policy for task offloading by maximizing the mean utility of the considered network. Extensive numerical results under different network conditions, along with comparison among the three algorithms, are presented to validate the feasibility of distributed reinforcement learning for task offloading in future IoV networks.

Reconfigurable intelligent surface (RIS) has emerged as important transmission technology to improve the spectral/energy efficiency in the next-generation (beyond 5G (B5G) and 6G) wireless communication network and has numerous applications in the areas of Internet of Things (IoT) and vehicular communication systems. Thus, in this paper, we investigate the application of RIS in unmanned aerial vehicle (UAV) enabled vehicular communication system with infinite and finite block length codes, where UAV communicates with the single antenna ground vehicle in the presence of several interfering vehicles on the road. We have obtained the approximate closed-form statistics of received SINR at ground vehicle in the presence of multiple nonidentical interference links. Furthermore, we analyze the performance of the considered system in terms of the coverage probability, bit-error-rate, block error rate (BLER) and goodput. It has been shown through the numerical results that the deployment of RIS significantly improves the performance of UAV-enabled vehicular communication network, even in the presence of the direct link between the UAV and the ground vehicle. Additionally, we also investigate the impact of various channel and system parameters like practical reflection coefficients of RIS, number of RIS reflecting elements, and number of interfering vehicles on the system performance. The analytical results are corroborated with Monte Carlo simulations.

This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system’s performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into$K$clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.

We consider a full-duplex (FD)-enabled multi-user two-way communication system with adaptive amplify-and-forward (AF)/decode-and-forward (DF) relaying protocol. All the users are assumed to be mobile and to have FD abilities. The effect of mobility, which results in time-selective fading, is modeled using a first-order autoregressive (AR1) process. The fading channel-based approach characterizes the residual self-interference (RSI) at the FD relay, user nodes and is modeled as a Rician distributed random variable. This paper constitutes the most critical use case of 5G, i.e., ultra-reliable low latency communication (URLLC), which adopts short-packets finite blocklength (FB) codes to spin out into the mission-critical applications where strict latency and reliability requirements are highly desirable. The outage performance of the system is studied over independent and non-identically distributed complex Gaussian (Rayleigh envelope) channels with imperfect channel state information (CSI) for with and without URLLC use cases. The closed-form expressions for the outage probability and block error rate (BLER) are derived for the absolute channel power-based scheduling scheme considering the different Doppler power spectra models and the effect of co-channel interference (CCI). The expressions for the asymptotic outage probability are also derived. The presented analysis is compared with baseline schemes, e.g., the results derived with adaptive AF/DF relaying are also compared with both AF and DF relaying, and the performance of the FD transmissions is compared to that of half-duplex (HD) transmissions. The impact of node mobility, RSI, FBL, number of user pairs, and imperfect CSI on the system performance is investigated. Moreover, essential insights are obtained related to the performance gain and region of the superiority of the adaptive AF/DF relaying scheme. The derived analytic results are validated through Monte Carlo simulations. Furthermore, at high transmit power, the outage performance for the adaptive AF/DF protocol approaches the derived asymptotic floor.

Ultra-reliable low-latency communication (URLLC) is one of the most important components in the fifth generation (5G) cellular networks for realizing mission-critical applications. In this paper, we jointly optimize the transceiver design and decoding error probability (DEP) of a full-duplex (FD) URLLC system, where the base station (BS) operates in FD mode, while the uplink (UL) and downlink (DL) users work in half-duplex (HD) mode. Accordingly, an optimization problem is formulated to maximize the achievable total (UL plus DL) rate for an FD URLLC system under finite blocklength, subject to the end-to-end (E2E) reliability constraint from the UL user to each DL user and the total transmission power constraint at the UL user and at the BS. We analyze the problem structure and convexify the problem by approximating the channel dispersion in scenarios of high and mid-to-high signal-to-interference plus noise ratio (SINR) regimes, respectively. Next, efficient iterative algorithms are proposed to find the near-optimal power allocation for the UL user and transceiver weights for the BS. Furthermore, closed-form expressions of the transceiver weights are derived, and the convergence of the proposed algorithms is proved. Simulation examples demonstrate the impact of the code blocklength, number of DL users, transmitter/receiver distortion and DEP threshold on the system performance.

We consider a multi-hop cooperative network inside a factory environment with the number of devices which are placed uniformly in a strip-shaped manner. the randomly deployed multiple intermediate relay nodes serve source and destination using the opportunistic large array (OLA) cooperative communication protocol for packet transmission. However, the simultaneous transmission of packets results in interference when the multiple transmissions occur simultaneously in the multihop wireless network. This paper analyzes the impact of such interference in the considered factory automation scenario from the Industry 4.0 perspective. We analyze the system performance in terms of the outage probability, success rate and latency with various network parameters. Moreover, the key insights are obtained related to the impact of packet insertion rate and tiers of interference on the system performance. Specifically, it is observed that the success rate of considered cooperative OLA network significantly increases due to the spatial diversity gains. However, the interference of multiple packets transmission severely affects the network success rate.

In this work, we investigate an unmanned aerial vehicle (UAV)-aided novel hybrid wireless communication network consisting of a cellular user and a small internet of things (IoT) network having one low-power IoT hub which serves a sensor node. During the first signalling phase, the UAV-assisted base station (BS) uses non-orthogonal multiple access signalling to serve the cellular user and the IoT-hub, simultaneously. In the second phase, the cellular user uplinks the control signal to UAV-BS, whereas, at the same time the IoT-hub communicates with the sensor node using the power harvested by applying simultaneous wireless information and power transfer (SWIPT) and nonlinear energy harvesting. We derive the closed-form expressions of the achievable ergodic capacity of the cellular user and the IoT-hub during the first signalling phase and the sensor node and the UAV-BS during the second phase considering nonlinear energy harvesting at the IoT hub. Further, we demonstrate the trade-off between available transmit power at UAV-BS, IoT-hub (harvested power), and cellular user to achieve desired capacity at the sensor node and UAV-BS. We validate the accuracy of derived expressions by using numerical simulations.

We consider an Internet of things (IoT) environment where the base station (BS) sends messages to the target user (TU) with the help of a cooperative user (CU). The cooperative user is selected based on the instantaneous signal-to-noise ratio (SNR) of the BS- TU link. For the considered cooperative IoT model, we further propose a new transmission protocol that incorporates the performance of active users and non-linear energy harvesting (EH) into account. The EH is assumed at every node, and the performance of the considered framework is evaluated in terms of outage probability. Specifically, for Rayleigh distributed fading channels, the closed-form expression for the outage probability is derived. The analytical results are validated through Monte-Carlo simulations. We also illustrated the impact of the number of CU and other parameters on the system's performance.

In this article, we investigate the nonlinear energy harvesting (EH)-based unmanned aerial vehicle (UAV)-assisted full-duplex (FD) Internet of Things (IoT) network with infinite and finite blocklength (FBL) codes. The reliability performance of the considered network, having two half-duplex UAVs and an FD IoT device, is analyzed in terms of the block error rate (BLER) with given ultrareliable and low-latency communication constraints. With the assumption of the combined effect of fading and shadowing, the closed-form expressions for BLER and network goodput are obtained over the Rician shadowed fading channels considering various shadowing scenarios, EH receiver architecture, IoT device mobility, inter-UAV interference, and self-interference (SI) cancelation capabilities at FD IoT device. The obtained results over the Rician shadowed fading for the nonlinear EH receiver architecture are also compared with the linear EH and over the Rician fading channels. The numerical results reveal important observations related to the impact of time-selective fading channels with imperfect channel state information, shadowing severity in the suburban areas, SI cancelation capabilities, blocklength, and the number of channel uses on the reliability performance of the UAV-assisted FD IoT network. Furthermore, the tightness of the approximation presented is verified through the Monte-Carlo simulations.

Intelligent reflecting surface (IRS) is being considered as a prospective candidate for next generation wireless communication due to its ability to significantly improve coverage and spectral efficiency by controlling the propagation environment. One of the ways IRS increases spectral efficiency is by adjusting phase shifts to perform passive beamforming. In this letter, we integrate the concept of IRS aided communication to the domain of multi-direction beamforming, whereby multiple receive antennas are selected to convey more information bits than existing spatial modulation (SM) techniques at any specific time. To complement this system, we also propose a successive signal detection (SSD) technique at the receiver. Numerical results show that the proposed design is able to improve the average successful bits transmitted (ASBT) by the system, which outperforms other state-of-the-art methods proposed in literature.

A reconfigurable intelligent surface (RIS)-assisted wireless communication system with non-linear energy harvesting (EH) and ultra-reliable low-latency constraints is considered for its possible applications in industrial automation. A distant data-center (DC) communicates with the multiple destination machines with the help of a full-duplex (FD) server machine (SM) and RIS. Assuming the deficiency of enough transmission power at the FD-SM, the SM is considered in the near vicinity of the destinations in the industry to forward the data received from the distant DC. The reception at SM is assisted by the RIS and a non-linear hybrid power-time splitting (PTS) based EH receiver architecture is adopted to extend the lifespan of SM, thus increasing network lifetime. The scheduling of multiple destinations is done by SM based on the considered selection criteria namely, random (RND) scheduling, absolute (ABS) channel-power-based (CPB) scheduling and normalized (NRM) CPB scheduling. The end-to-end performance of the considered FD RIS-assisted network is analyzed, and the expressions for the block error rate (BLER) for all scheduling schemes are derived. Moreover, the effects of number of RIS elements, packet size, channel uses on the system performance are analyzed for the considered ultra-reliable and low-latency communication (URLLC) network. The scheduling fairness of all the scheduling schemes is also analyzed to study the performance-fairness trade-off. The derived analytical results are verified through Monte-Carlo simulations.

In this paper, a multi-user full-duplex (FD) two-way relaying system with decode-and-forward protocol is considered where all the nodes are assumed to be mobile and to have FD abilities. The residual self-interference (RSI) at each node is modeled as a Rician distributed random variable and the effect of mobility is incorporated by adopting the first order auto-regressive (AR) model. Then, the signal-to-interference-plus-noise-ratio (SINR) based scheduling schemes namely random scheduling, absolute channel power-based (CPB) scheduling, hybrid scheduling, and normalized CPB scheduling, are modified and the user pair selection is carried out on the basis of instantaneous channel statistics. The performance of the considered framework is analyzed for the modified scheduling schemes in terms of error probability and average rate. Next, the closed-form expressions for the symbol error probability (SEP) and average rate are derived for independent and non-identically distributed Rayleigh fading channels. Also, the numerical analysis highlighting the impact of user mobility and imperfect channel estimation on the scheduling strategies, fairness is presented. Furthermore, the impacts of the degree of traffic asymmetry, scheduling fairness, user mobility, channel estimation errors and RSI on the system performance are investigated. Numerical results are validated through the Monte Carlo simulations.

Ultra-reliable and low-latency communication (URLLC) is one of the key applications in fifth generation (5G) cellular networks, which requires extremely high reliability (~99.9999%) and low latency (<; 1 ms). In this paper, the energy efficiency (EE) of multi-user multicarrier amplify-and-forward (AF) networks is maximized under short packet transmission. Accordingly, we formulate an energy-efficient resource allocation problem to jointly optimize the transmit power, subcarrier pairing and allocation, and error probability with finite block-length codes subject to the constraints of the decoding error probability of each user pair, subcarrier pairing and allocation and total transmission power. The formulated problem is non-convex and hence difficult to solve. We analyze the structure of the problem and hence convert it into a convex problem which is approximately equivalent to the original one. An efficient algorithm is also proposed which is capable of producing a near-optimal solution. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC, by showing the impact of various system parameters on EE.

One of the key applications in the fifth generation (5G) communication systems is to support extremely high reliability (~ 99.999%) and low latency (<; 1 ms), namely ultra-reliable and low-latency communication. In this paper, we consider the problem of maximizing energy efficiency (EE) for downlink multi-user multiple-input single-output (MISO) networks under short packet transmission. An optimization problem is formulated to jointly optimize the precoders at the base station (BS) for serving multiple downlink users and the error probability with finite blocklength (FBL) codes, subject to the constraints on decoding error probability per URLLC user and on the BS transmit power. Since the formulated problem is non-convex, we convert this problem into a convex one by analyzing the structure of the EE objective. We then propose an algorithm to find a near-optimal solution for maximizing the EE. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC.

In this paper, we jointly optimize the transceiver design and decoding error probability of a full-duplex (FD) ultrareliable low-latency communication (URLLC) system, where the base-station (BS) operates in an FD mode while the uplink (UL) and downlink (DL) users work in a half-duplex (HD) mode. Accordingly, an optimization problem is formulated for an FD URLLC system under the finite blocklength (FBL) to maximize the achievable total (UL plus DL) rate subject to the reliability (i.e., the decoding error probability) of each link and total transmission power constraints at the UL user and the BS. We convexify the formulated non-convex problem by analyzing the problem structure. Next, an efficient iterative algorithm is proposed to find the near-optimal power allocation for the UL user and the transceiver weights for the BS. Simulation examples show the impact of the blocklength and decoding error probability on the system performance.

Spectrum sharing and full duplexing are two promising technologies for alleviating the severe spectrum crunch that has threatened to blight the progress of future wireless communication systems. In this paper, we consider a two tier coexistence framework involving a collocated multiple-input-multiple-output (MIMO) radar system (RS) and a full-duplex MIMO cellular system (CS). Considering imperfect channel state information and hardware impairments at the CS, we focus on a spectrum sharing environment to improve the quality of service (QoS) for cellular users by designing i) precoders at CS via the minimization of sum mean-squared-errors, subject to the constraints of transmit powers of the CS and probability of detection (PoD) of the RS, and ii) precoders at the RS to mitigate the interference from RS towards CS. While the monotonically increasing relationship between PoD and its non-centrality parameter is exploited to resolve the PoD in terms of interference threshold towards the RS, a generalized likelihood ratio test for target detection is used to derive detector statistics of the precoded radar waveforms. Numerical results demonstrate the feasibility of the proposed spectrum sharing algorithms, albeit with certain tradeoffs in RS transmit power, PoD and QoS of cellular users.

In this paper we investigate the co-existence between a multi-cell multi-user (MU) full-duplex (FD) multiple-input multiple-output (MIMO) cellular network and a MIMO radar. While a joint beam-forming design technique at the cellular base-stations and users is proposed to maximize the detection probability of the MIMO radar subject to constraints of data rate per user in each cell and transmit power, null-space based waveform projection is used to mitigate the interference from the radar towards the cellular network. In particular, the proposed technique optimizes the performance of detection probability by maximizing its lower bound, which is obtained by exploiting the monotonically increasing relationship of detection probability and its non-centrality parameter. Numerical results show the feasibility of spectrum sharing between both systems.

In this paper, subcarrier pairing and power allocation are jointly investigated to maximize the energy efficiency (EE) of a dual-hop multicarrier decode-and-forward (DF) relay network. The optimization problem is formulated as a ratio of the spectrum efficiency (SE) over the entire power consumption of the network subject to total power and subcarrier pairing constraints. A near-optimal iterative scheme is proposed to perform the subcarrier pairing and power allocation for achieving the maximum EE of the network. A two-step suboptimal resource allocation scheme is further proposed to reduce the complexity, in which the subcarrier pairing is first performed by considering the channel quality of the source-to-relay (SR) and relay-to-destination (RD) links, followed by an energy-efficient power allocation scheme to maximize the EE. Numerical results are presented to confirm the effectiveness of the proposed schemes and to demonstrate the tradeoff between EE and SE performances.

In this paper, we investigate the feasibility of wireless edge caching in a hybrid millimeter-wave (mmWave)- micro-wave (μWave) network. Considering the average success probability (ASP) of file delivery as the performance metric, we derive expressions for the association probability of the typical user to the mmWave and μWave networks using stochastic geometric tools. Accordingly, we provide an upper bound on the ASP of file delivery and formulate the content caching placement scheme as an optimization problem with respect to caching probabilities, which jointly optimizes the ASP of file delivery considering both content placement and delivery phases. To simplify the non-convex problem and obtain design insights, we split it into two scenarios: 1) noise-limited and 2) interference-limited, and then propose optimal caching placement algorithms for both. We numerically evaluate the performance of the proposed schemes under several essential factors, such as content popularity, cache size, target data rate, blockages in the mmWave network, BS density, and path loss and also compare it with other common proactive caching schemes, namely uniform caching, caching M most popular files, and random caching. Numerical results demonstrate the superiority of the proposed caching scheme over others, albeit certain trade-offs.

In this paper, we investigate a full duplex (FD) multi-user non-orthogonal multiple access (NoMA) communication system based on the optimization of received signal-to-interference-plus-noise ratio (SINR) per unit power. Since the communication system operates in the FD mode, co-channel interference (CCI) and self-interference (SI) dominate the system's performance. Accordingly, to combat the CCI, we adopt a game-theoretic approach and propose users' clustering algorithms and to suppress the SI, we formulate an optimization problem to maximize the power-normalized SINR (PN-SINR). While the user clustering optimization problem is constrained by: 1) the successive interference cancellation (SIC) constraint and 2) two binary constraints for the allocations of uplink (UL) and downlink (DL) users, the PN-SINR problem is constrained by: 1) total transmit power budget at the base station and UL users; 2) the fundamental condition for the implementation of successive interference cancellation in the NoMA; and 3) the minimum fairness condition for the UL users. The original PN-SINR problem is non-convex and hence is converted into an equivalent subtractive-form problem, after which we propose an iterative algorithm to find the optimal power allocation policy. Properties of all the proposed algorithms are thoroughly investigated and the numerical results are provided. Based on the channel conditions and suppression level of SI and CCI, the superiority of the proposed FD-NoMA system over half-duplex NoMA and FD orthogonal multiple access systems is verified.

In this paper, spectrum sharing between a multiple-input-multiple-output (MIMO) radar and a full duplex (FD) multi-user MIMO (MU-MIMO) cellular system is studied. While a joint transceiver design technique at a hardware impaired FD cellular system considering imperfect channel state information is developed to maximize the detection probability of the MIMO radar, null-space based waveform projection is proposed to mitigate the interference from the radar towards the cellular system. The proposed technique exploits the monotonically increasing relationship of detection probability and its non-centrality parameter and accordingly optimizes the performance of detection probability of the radar, while also providing the cellular users with specific quality-of-service (QoS). Numerical results demonstrate the feasibleness of spectrum sharing between both systems, albeit certain trade-offs in radar transmit power, detection probability and QoS of users in the cellular system.

We propose a novel transceiver design technique to facilitate flexible spectrum sharing between a multiple-input multiple-output (MIMO) radar and a full-duplex (FD) MIMO cellular system. The optimization problem for maximizing the rate of the cellular system is formulated, subject to the constraints of individual power at the uplink users, total power at the base station, and interference power towards the MIMO radar from the cellular system so that the detection probability of the radar is not hindered. We show that the above problem can be cast as a second-order cone programming problem and the joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results show that using the spectrum shared by the radar, the FD cellular system can achieve sum rate of up to 25-30 bits/sec/Hz for a reasonable self-interference cancellation of around -70 dB. However, to facilitate this, while also maintaining a detection probability of around 0.9, the radar needs to spend an extra power of around 2-3 dB.

In this paper, the feasibility of spectrum sharing between a multiple-input multiple-output (MIMO) radar system (RS) and a MIMO cellular system (CS), comprising of a full-duplex (FD) base station (BS) serving multiple downlink and uplink users at the same time and frequency is investigated. While a joint transceiver design technique at the CS's BS and users is proposed to maximize the probability of detection (PoD) of the MIMO RS, subject to constraints of quality of service (QoS) of users and transmit power at the CS, null-space based waveform projection is used to mitigate the interference from RS toward CS. In particular, the proposed technique optimizes the performance of PoD of RS by maximising its lower bound, which is obtained by exploiting the monotonically increasing relationship of PoD and its non-centrality parameter. The numerical results show the utility of the proposed spectrum sharing framework, but with certain tradeoffs in performance corresponding to RS's transmit power, RS's PoD, CS's residual self-interference power at the FDBS and QoS of users.

The rapid growth of energy consumption due to the strong demands of wireless multimedia services, has become a major concern from the environmental perspective. In this paper, we investigate a novel energy-efficient resource allocation scheme for relay-assisted multiuser networks to maximize the energy efficiency (EE) of the network by jointly optimizing the subcarrier pairing permutation formed in one-to-many/many-to-one manner, subcarrier allocation, as well as the power allocation altogether. By analyzing the properties of the complex mixed-integer nonlinear programming problem, which is generally very difficult to solve in its original form, we transform the problem into an equivalent convex problem by relaxing the integer variables using the concept of subcarrier time sharing, and by applying a successive convex approximation approach. Based on the dual decomposition method, we derive an optimal solution to the joint optimization problem. The impact of different network parameters, namely number of subcarriers and number of users, on the attainable EE and spectral efficiency (SE) performance of the proposed design framework is also investigated. The numerical results are provided to validate the theoretical findings and to demonstrate the effectiveness of the proposed algorithm for achieving higher EE and SE than the existing schemes.

In this paper, we study an amplify-and-forward relay network with energy harvesting (EH) source and relay nodes. Both nodes can continuously harvest energy from the environment and store it in batteries with finite capacity. Additionally, the source node is capable of transferring a portion of its energy to the relay node through a dedicated channel. The network performance depends on not only the energy arrival profiles at EH nodes but also the energy cooperation between them. We jointly design power control and transfer for maximizing the sum rate over finite time duration, subject to energy causality and battery storage constraints. By introducing auxiliary variables to confine the accumulated power expenditure, this non-convex problem is solved via a successive convex approximation approach, and the local optimum solutions are obtained through dual decomposition. Also, when channels are quasi-static and the power control values of the source (relay) node are preset to a constant, a monotonically increasing power control structure with the time is revealed for the relay (source) node with infinite battery capacity. Computer simulations are used to validate the theoretical findings and to quantify the impact of various factors, such as EH intensity at nodes and relay position on the sum rate performance.

The rapid growth in energy consumption due to strong demands of wireless multimedia services, has become a major concern from an environmental perspective. In this paper, we investigate a novel energy-efficient resource allocation scheme for relay-assisted multiuser networks to maximize the energy efficiency (EE) of the network by jointly optimizing the subcarrier pairing permutation formed in one-to-many/many-to-one manner, subcarrier allocation, as well as the power allocation all together. By analyzing the properties of the complex mixed-integer nonlinear programming problem, which is generally very difficult to solve in its original form, we transform the problem into an equivalent convex problem by relaxing the integer variables using the concept of subcarrier time sharing, and by applying a successive convex approximation approach. Based on the dual decomposition method, we derive an optimal solution to the joint optimization problem. Numerical results are provided to validate the theoretical findings and to demonstrate the effectiveness of the proposed algorithms.

In this paper, we investigate resource allocation algorithm for multiuser full-duplex (FD) decode-and-forward (DF) relay network for improving the energy efficiency (EE). The problem of energy-efficient joint subcarrier and power allocation is formulated as a ratio of the spectral efficiency (SE) of the network to the total energy consumption in the network. The formulated problem is a non-convex fractional mixed binary-integer non-linear programming problem. Further, we resolve this problem by a series of convex transformations and introduce a network penalty factor, which works as EE parameter, to convert the fractional mixed-integer form into a parametric subtractive form, and then design an effective energy-efficient iterative resource allocation algorithm to find the optimal solution. In addition, we also propose a low-complexity suboptimal algorithm. The effectiveness of the proposed iterative and suboptimal algorithms are evinced by simulation results.

In this paper, we investigate an energy-efficient joint subcarrier and power allocation algorithm for improving the worst energy efficiency (worst-EE) in multiuser two-way amplify-and-forward (AF) relay networks, that not only balances the EE of the two-way links, but also ensures the desired quality-of-service (QoS) of each user. In order to maximize the worst-EE, an EE-balancing optimization problem is formulated as the ratio of the spectral efficiency (SE) over the total network power consumption subject to a total transmit power and users' QoS constraints. Since the formulated primal problem is a non-convex fractional mixed binary-integer program, i.e., NP-hard to solve, thus a concave lower bound on the objective function and a series of convex transformations are applied to transform the problem into a tractable convex form. An iterative algorithm is proposed to achieve the optimal solution of the dual problem. We further propose an efficient, low-complexity suboptimal resource allocation algorithm. Simulation results demonstrate the effectiveness of the proposed iterative and suboptimal algorithms.

A perturbation-based power allocation and multirelay selection approach is proposed for multiple-input multiple-output relay networks in multipath fading channels. In this approach, the relays are partitioned into two groups according to Lagrangian multipliers of power constraints. The power allocation for the relays is perturbed by increasing the power for the potential relay's group, while decreasing the power of the relays in the other group. An optimization framework is then formulated as a tradeoff between the relay selection and the mean square error performance degradation. Computer simulations are used to demonstrate the performance.

In this paper, we investigate utility-based joint subcarrier and power allocation algorithms for improving the energy efficiency (EE) in multi-user two-way regenerative relay networks. With the objective of determining the best subcarrier allocation for each user pair, subcarrier pairing permutation, and power allocation to all the nodes, a network price is introduced to the power consumption as a penalty for the achievable sum rate, followed by the examination of its impact on the tradeoff between the EE and spectral efficiency. The formulated optimization problem is a non-convex mixed-integer nonlinear programming problem, and thus a concave lower bound on the objective function and a series of convex transformations are applied to transform the problem into a convex one. Through dual decomposition, we propose a utility-based resource allocation algorithm for iteratively tightening the lower bound and finding the optimal solution of the primal problem. By exploring the structure of the obtained optimal solution, an optimal price that enables green resource allocation is found from the perspective of maximizing EE. Additionally, a suboptimal algorithm is investigated to strike a balance between computational complexity and optimality. Simulation results evince the effectiveness of the proposed algorithms.

In this paper, a novel resource allocation algorithm is investigated to maximize the energy efficiency (EE) in multiuser decode-and-forward (DF) relay interference networks. The EE optimization problem is formulated as the ratio of the spectrum efficiency (SE) over the entire power consumption of the network subject to total transmit power, subcarrier pairing, and allocation constraints. The formulated problem is a nonconvex fractional mixed binary integer programming problem, i.e., NP-hard to solve. Furthermore, we resolve the convexity of the problem by a series of convex transformations and propose an iterative EE maximization algorithm to jointly determine the optimal subcarrier pairing at the relay, subcarrier allocation to each user pair and power allocation to all source and the relay nodes. Additionally, we derive an asymptotically optimal solution by using the dual decomposition method. To gain more insights into the obtained solutions, we further analyze the resource allocation algorithm in a two-user case with interference-dominated and noise-dominated regimes. In addition, a suboptimal algorithm is investigated with reduced complexity at the cost of acceptable performance degradation. Simulation results are used to evaluate the performance of the proposed algorithms and demonstrate the impacts of various network parameters on the attainable EE and SE.

In this paper, we study the problem of energy-efficient resource allocation in multiuser two-way amplify-and-forward (AF) relay networks with the aim of maximizing the energy efficiency (EE), while ensuring the quality-of-service (QoS) requirements and balancing the EE of the user links. We formulate an EE-balancing optimization problem that maximizes the ratio of the spectral efficiency (SE) over the total power dissipation subject to QoS and a limited transmit power constraints. The problem which maximizes the EE by jointly optimizing the subcarrier pairing, power allocation, and subcarrier allocation, turns out to be a non-convex fractional mixed-integer nonlinear programming problem, which has an intractable complexity in general. We apply a concave lower bound on the achievable sum rate and a series of convex transformations to make the problem convex one and propose an iterative algorithm for iteratively tightening the lower bound and finding the optimal solution through dual decomposition approach. In addition, a low-complexity suboptimal algorithm is investigated. We then characterize the impact of various network parameters on the attainable EE and SE of the network employing both EE maximization and SE maximization algorithms when the network is designed from the EE perspective. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this paper, energy-efficient resource allocation algorithms are investigated to improve the energy efficiency (EE) in two-way multi- carrier amplify-and-forward (AF) relay networks by ensuring the quality-of-service (QoS) and balancing the EE of the user links. We formulate an EE-balancing optimization problem that maximizes the ratio of the spectral efficiency (SE) and the total network power consumption by jointly designing the subcarrier pairing at the relay node and power allocation at all nodes under the total transmit power and QoS constraints, thereby leading to a mixed integer programming problem which turns out to be non-convex. Further, we resolve the problem by a series of convex transformations and propose an iterative EE algorithm to determine the solution through Lagrangian dual decomposition. Moreover, a suboptimal EE algorithm is also investigated with reduced complexity at the cost of acceptable performance degradation. Simulation results validate the performance gain of the proposed algorithms and show the performance tradeoff between EE and SE.

Energy harvesting and wireless energy transfer are capable of relieving the battery limitation of wireless devices. In this paper, an amplify-and-forward relay network (AF-RN) is considered, where an energy harvesting source node communicates with a destination node through an energy harvesting relay node. To further improve the performance, the relay is allowed to harvest energy from the radio frequency (RF) signals sent by the source node through a dedicated energy control channel. A joint power control and energy transfer scheme is investigated with the goal of maximizing the achievable sum rate by a deadline subject to energy causality constraints. The problem is challenging in that the objective function is non-convex, and a successive convex approximation approach is proposed to achieve the optimal power control and energy transfer solution. Finally, numerical examples are given to demonstrate the effectiveness of our proposed algorithm.

In this paper, we investigate an energy-efficient power allocation scheme in the cognitive radio (CR) networks that utilizes the energy harvested from ambient sources and spectrum sensing information. The optimization problem is formulated as a ratio of the spectral efficiency (SE) to the total energy consumption under the energy and battery causality constraints, which is solved by an iterative algorithm to obtain the optimal solutions. Moreover, the expressions for the optimal solutions are obtained through the dual-decomposition method. Finally, with the help of exhaustive simulation results we show that the proposed power allocation scheme immoderately improves the the average energy efficiency (EE) and SE performance of the network.

In this paper, we focus on designing energy-efficient power allocation schemes to improve the energy efficiency (EE) in multiuser multi-carrier two-way relay networks which are able to not only balance the EE of the two-way links but also ensure the quality-of-service (QoS). Specifically, the proposed design framework attempts to maximize a ratio of the spectral efficiency (SE) over the total network power consumption under a total power constraint as well as a signal-to-interference plus noise power ratio (SINR) constraint. The original problem is indeed non-convex, and we prove the convexity of the problem after a series of convex transformation. An iterative approach is proposed to find the local optimal solution of the original problem for achieving the maximum EE. Simulation results are provided to demonstrate the tradeoff between the EE and the SE.

Green communications have emerged as a demanding concept for improving the network energy efficiency (EE). In this paper, a pricing-based approach is investigated to achieve energy-efficient power allocation in relay-assisted multiuser networks. We introduce a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the EE and the spectral efficiency (SE). It is hard to directly solve the problem as it is non-convex, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. We further analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches, etc. Finally, the performance of the proposed approach is evaluated by computer simulations, and we characterize the interaction between the EE and SE for various network parameters when the network is designed from the energy-efficient perspective.

Energy efficiency is a growing concern for the future wireless networks as energy consumption becomes a global environment problem. In this paper, an energy-efficient power control scheme is investigated for achieving the maximum energy efficiency in multiuser two-way balancing relay networks. We formulate the design problem as a ratio of the spectral efficiency over the entire energy consumption of the network under a total power constraint. An optimal power control scheme is proposed to iteratively improve the efficiency and finally reach the globally optimal solution. Compared with a heuristic scheme where the total available power is equally distributed among all nodes, the proposed scheme can dramatically improve not only the energy efficiency but also the spectral efficiency.

The rapid growth of diversified applications has led to significant increase in data traffic and energy consumption in wireless networks. Hence, the energy efficiency becomes one of critical performance indices for designing next-generation wireless networks. In this paper, an optimization framework of power allocation is investigated for maximizing energy efficiency in multiuser relay networks. Under a total power constraint, we formulate the design problem with the objective as the ratio of the spectral efficiency over the entire power consumption of the network. An optimal energy-efficient power allocation algorithm is proposed for the source and the relay nodes to approach the maximum efficiency in an iterative manner. Compared with a heuristic scheme where the total available power is equally allocated to all nodes, the proposed optimal power allocation algorithm can dramatically improve energy efficiency with a slight loss in spectral efficiency.

The deployment of relays has been regarded as an effective means to improve the performance of the conventional wireless networks. The achievable performance generally depends on the availability of channel state information (CSI) at the relay and the destination nodes. The requirement of the dual-hop CSIs at the destination node, however, results in high computational complexity and unrealistic assumption for mobile relay networks, especially when the source-to-relay channel fluctuates rapidly due to the mobility of the source node. In this paper, we design an amplify-and-forward (AF) mobile relay network in which the instantaneous CSI of the source-to-relay link is unknown to the destination node. A composite hypothesis testing problem is formulated to derive the optimal detector. Unlike the existing approaches, the designed mobile relay network does not require excessive signalling overhead for obtaining the instantaneous CSI of the source-to-relay link at the destination while offering a significant performance improvement over the conventional wireless networks without the help from relays. The obtained performance even approaches that of the relay networks with full CSIs. When considering both implementation complexity issues and performance enhancement, the proposed detection scheme provides an alternative way of developing a low-complexity mobile relay networks.

This paper jointly designs power allocation and two-dimensional (2-D) equalizers for multiple relay nodes in a distributed multiple-input multiple-output (MIMO) relay network. Based on the minimum mean-square error (MMSE) criterion, 2-D temporal-and-spatial equalizers are investigated at relays with a total equalizer power constraint for equalizing-and-forwarding the signals from the source to the destination in multipath fading channels, which has not been addressed in the existing literature. A bisection algorithm is proposed to obtain the optimal solution by utilizing the Karush-Kuhn-Tucker (K.K.T.) conditions. With the proposed 2-D equalizers, the distributed MIMO relay network can not only effectively mitigate the inter-symbol interference (ISI) and multiple-antenna interference (MAI) but also achieve the spatial and multipath diversity gains. Simulation results show that the proposed scheme can provide a substantial performance gain in terms of the bit error rate (BER) as compared with the conventional one-tap equalizer scheme for distributed MIMO relay systems.

This paper considers an equalize-and-forward (EF) strategy for two-hop multiple-input multiple-output (MIMO) relay networks in multipath fading channels, where the relay nodes and the destination only know its respective backward channel state information (CSI) knowledge, and each node is equipped with multiple antennas for transmitting, receiving or forwarding signals. For such a relay network, the inter-symbol interference (ISI) and multiple-antenna interference (MAI) are two detrimental effect to degrade the bit error rate (BER) performance. In order to compensate for the interference problem, we design temporal-and-spatial (TS) equalizers to assist in forwarding and decoding the signals at the relay nodes and the destination node, respectively. Based on the minimum mean square error (MMSE) criterion with a total power constraint, an optimization framework is formulated to find the optimal TS equalizers with the backward CSI knowledge, and an iterative algorithm using the Karush-Kuhn-Tucker (K.K.T.) conditions is investigated to achieve the optimal solution. With these optimal TS equalizers, the MIMO relay network can not only effectively mitigate the interference but also provide both the spatial and multipath diversity gains. Simulation results indicate the effectiveness of the proposed algorithm, yielding a significant improvement on the BER performance.