In this paper, we propose a framework for energy-efficient (EE) design in reconfigurable intelligent surface (RIS)-assisted multi-satellite Internet of Things (IoT) networks, taking into account the imperfect channel state information (CSI). In this framework, the multi-satellite network is used to enhance communication capabilities, while the RIS is deployed to further improve EE performance. Our objective is to maximize the EE of the proposed network by jointly optimizing the active beamforming and scheduling of the satellites and the phase shifts of RIS under the transmit power constraint for each satellite, the elevation angle constraints, and the phase shift constraints of the RIS. To handle this non-convex and NP-hard optimization problem, we propose two efficient algorithms, i.e., the Dinkelbach-BigM-Successive-Penalty (DBSP) algorithm and the Lagrangian Dual Majorization (LDM) algorithm. The DBSP algorithm is based on the alternating optimization approach, which can effectively solve the formulated non-convex optimization problem with multiple dual and complex optimization variables. Specifically, we first employ the Dinkelbach method, successive convex approximation, big-M formulation, and semidefinite relaxation method to optimize the active beamforming and the scheduling of the satellites. In addition, the penalty convex-concave procedure approach is utilized to design the phase shifts of RIS. To reduce the complexity and improve computational efficiency, we propose the LDM algorithm and derive an analytical solution for active beamforming and phase shifts by exploiting the Lagrangian dual transform, quadratic transform, and majorization-minimization algorithms. Numerical simulations are conducted to demonstrate the efficiency and convergence behavior of the proposed algorithms. Moreover, it is also demonstrated that the proposed algorithms are superior to other benchmarks, corroborating the benefits of deploying an RIS in the multi-satellite network.

In recent years, unmanned aerial vehicles (UAVs) have become a key role in wireless communication networks due to their flexibility and dynamic adaptability. However, the openness of UAV-based communications leads to security and privacy concerns in wireless transmissions. This paper investigates a framework of UAV covert communications which introduces flexible reconfigurable intelligent surfaces (F-RIS) in UAV networks. Unlike traditional RIS, F-RIS provides advanced deployment flexibility by conforming to curved surfaces and dynamically reconfiguring its electromagnetic properties to enhance the covert communication performance. We establish an electromagnetic model for F-RIS and further develop a fitted model that describes the relationship between F-RIS reflection amplitude, reflection phase, and incident angle. To maximize the covert transmission rate among UAVs while meeting the covert constraint and public transmission constraint, we introduce a strategy of jointly optimizing UAV trajectories, F-RIS reflection vectors, F-RIS incident angles, and non-orthogonal multiple access (NOMA) power allocation. Considering this is a complicated non-convex optimization problem, we propose a deep reinforcement learning (DRL) algorithm-based optimization solution. Simulation results demonstrate that our proposed framework and optimization method significantly outperform traditional benchmarks, and highlight the advantages of F-RIS in enhancing covert communication performance within UAV networks.

Unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) systems are envisioned to simultaneously provide secure data transmission and nighttime illumination. However, when buildings obstruct the optical links, both connectivity and lighting are disrupted, which may severely compromise system reliability and safety. This paper investigates an artificial noise-based physical layer security (PLS) scheme for a VLC-enabled UAV communication system in a multiuser environment with potential eavesdroppers, while explicitly incorporating awareness of shadowed area caused by blockage and enabling the UAV to autonomously adjust its trajectory to proactively avoid such shadow coverage to the ground users. We formulate a joint optimization problem of user association, power allocation, and UAV trajectory design to maximize the average secrecy rate of the system, while taking into account illumination requirements, shadowing effects, and UAV mobility. To tackle this mixed-integer and non-convex optimization problem, we decompose it into three subproblems and transform them into tractable convex forms. Furthermore, we also develop an iterative algorithm by leveraging successive convex approximation techniques under a block coordinate descent framework to efficiently obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can achieve fast convergence and improve the average secrecy rate at least by 51.1% compared with conventional schemes. Moreover, the algorithm still exhibits robustness and efficacy in exploiting the spatial-temporal trade-offs under severe eavesdropping threats and shadowing with diverse user geometries, highlighting its practicality for secure nighttime urban VLC-UAV communication.

This paper investigates distributed link selection (LS) for a multi-hop buffer-aided relay network consisting of one source, one destination, and multiple relays, where each node has access only to local instantaneous channel state information (CSI) and the buffer status of its adjacent nodes. In particular, by improving the alternate-transmission strategy, we propose a novel flexible distributed LS scheme that flexibly selects odd- and even-numbered links in each time slot. Notably, acquisition of local buffer-state information is integrated into the distributed LS agreement process, so it incurs no extra signaling overhead. We also derive the average throughput and packet delay of the proposed scheme by constructing a two-layer Markov model and enumerating all feasible buffer-state transitions. In addition, a simplified expression and an asymptotic analysis are provided to give further insight into the average throughput. Theoretical analysis and simulations show that the proposed flexible scheme substantially outperforms a baseline alternate scheme and closely approaches the performance of a related centralized LS scheme.

This paper proposes a novel covert transmission framework for an unmanned aerial vehicle (UAV)-reconfigurable holographic surface (RHS)-aided full-duplex (FD) integrated sensing and communication (ISAC) system, where the aerial access point (AP) simultaneously performs target sensing and downlink covert communication. We jointly design the AP’s downlink transmit signal and uplink receive beamformers, the RHS weights, the users’ uplink transmit powers, and the UAV’s trajectory, considering imperfect knowledge of the warden’s channel state information (CSI). An optimization problem is formulated to maximize the minimum covert transmission rate (CTR) among all downlink covert users (DCUs), subject to constraints on required sensing and uplink transmission capabilities, covertness, and total power budget. To tackle the intractable non-convex problem, we leverage the Bernstein-type inequality, majorization-minimization (MM), and successive convex approximation (SCA), and propose a secure optimization framework that efficiently updates all variables using convex optimization techniques. To further understand the proposed algorithm, its convergence behavior and computational complexity are discussed. Simulation results demonstrate that integrating RHS and UAV techniques into the optimization design enhances the covert transmission performance of FD-ISAC systems while ensuring a certain level of sensing capability.

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple-input–multiple-output AFDM (MIMO-AFDM) systems. Firstly, we introduce a unified variational inference (VI) approach to approximate the target posterior distribution, under which the belief propagation (BP) and expectation propagation (EP)-based algorithms are derived. As both VI-based detection and low-density parity-check (LDPC) decoding can be expressed by bipartite graphs in MIMO-AFDM systems, we construct a joint sparse graph (JSG) by merging the graphs of these two for low-complexity receiver design. Then, based on this graph model, we present the detailed message propagation of the proposed JSG. Additionally, we propose an enhanced JSG (E-JSG) receiver based on the linear constellation encoding model. The proposed E-JSG eliminates the need for interleavers, de-interleavers, and log-likelihood ratio transformations, thus leading to concurrent detection and decoding over the integrated sparse graph. To further reduce detection complexity, we introduce a sparse channel method by approaximating multiple graph edges with insignificant channel coefficients into a single edge on the VI graph. Simulation results show the superiority of the proposed receivers in terms of computational complexity, detection and decoding latency, and error rate performance compared to the conventional ones.

An optical reconfigurable intelligent surface (ORIS) was expected to offer extra secrecy performance gain in a visible light communication (VLC) system. However, nonlinear impairments involved degrade the confidential signal reception and have not been fully considered in designing physical layer security (PLS). In this paper, a novel PLS approach is proposed for an ORIS-aided VLC system with consideration of practical nonlinear impairments. It is mathematically formulated to be an optimization problem that maximizes the signal-to-interference-plus-distortion-and-noise ratio of the legitimate link, while entirely suppressing that of multiple eavesdroppers by jointly optimizing the beamforming, jamming and clipping at the transmitters, and also the surface configuration in terms of mirror assignments and rotation angles at the ORIS. We decompose this mixed combinatorial and non-convex optimization problem into three sub-problems and elaborately transform them to be conventional convex programming, quadratic programming and nonlinear programming problems, respectively. Moreover, we also develop a time-efficient iterative approach to achieve the suboptimal solution with low-computational complexity. Simulation results demonstrate the improvement of secrecy performance as compared with conventional schemes, and also the robustness to severe nonlinear impairments and spatial correlation, thereby confirming the beneficial insights of this methodology for secure VLC with nonlinear devices.

In this paper, we investigate a reconfigurable intelligent surface (RIS) aided spectrum sharing scheme between a multiple input multiple-output (MIMO) radar and MIMO multiuser communication system under non-homogeneous interference scenarios, including the interference from scattering points, the mutual interference between the two systems, and the interference among multiple users. We consider a flexibly-weighted framework for joint resource allocation, aiming at maximizing the mutual information of both the radar and the communication systems under the usual constraints on the transmit power and on the compatibility of the space spectral. To deal with the resulting triple degrees of freedom non-convex framework, a sub-optimal procedure, based on iterative alternating maximization of three suitably derived subproblems, is proposed and analyzed. Each yields a closed-form solution. In particular, to address the constant modulus constraint imposed by the RIS, we propose two different optimization strategies, based on the Minorization-Maximization framework in conjunction with the Alternating Direction Penalty Method and the Element Block Coordinate Descent formulations, namely, MM-ADPM and MM-EBCD, respectively. Finally, simulation results compare the effectiveness and advantages of the two algorithms.

Physical layer security (PLS) has emerged as a promising technology to protect critical and sensitive information against unauthorized devices. To address the key challenge of acquiring channel state information (CSI) of passive eavesdroppers in PLS implementation, we propose a novel sensing-assisted PLS scheme with the aid of reflecting reconfigurable intelligent surface (STAR-RIS). It employs a self-refine sensing scheme utilizing the artificial noise (AN) signals to iteratively estimate the eavesdroppers’ positions for CSI calculation. We aim to maximize the secrecy capacity based on the sensing-estimated CSI while tracking the eavesdroppers in full-duplex (FD) mode with integrated sensing and communication (ISAC) signals comprising artificial noise (AN). This is achieved by jointly designing the beamforming vector of information signals, the beamforming vector of AN signals, and the coefficients of the STAR-RIS. To optimize these coupled variables, we introduce an alternating optimization (AO) scheme to solve the problem recursively. In particular, we tackle the non-convexity of the beamforming optimizations for information and AN signals with the successive convex approximation (SCA) scheme and adopt a semi-definite relaxation (SDR) scheme to design the reflection and refraction coefficients of the STAR-RIS. The numerical results validate that the proposed scheme ensures secure communications against multiple eavesdroppers without any prior eavesdropper channel information. In addition, the proposed scheme can significantly improve SC performance by up to 66. 7% compared to the benchmarks without the sensing-assisted function.

In this paper, we propose a federated learning (FL) system enhanced by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which is designed to protect client-side data privacy and reinforce the security of data exchange over wireless channels. Assuming an honest-but-curious central server that may infer private information from user gradients, we adopt differential privacy (DP) by injecting noise into local updates to safeguard user data. Theoretical results are derived to characterize the systematic privacy guarantees provided by the DP noise power and the gradient information in the proposed STAR-RIS-enabled DP-FL systems. Building on these results, the secrecy sum rate of local information is formulated by jointly optimizing the STAR-RIS coefficient matrices, users’ transmission power, artificial jamming power, and the power of DP noise introduced by the FL users. To tackle the non-convex optimization challenge, we develop a block coordinate descent algorithm that partitions the original problem into four manageable subproblems. The closed-form expressions are obtained for users’ transmit power, artificial jamming power, and the power of DP noise. For the STAR-RIS phase shift design, approximate solutions are derived through semidefinite relaxation combined with a surrogate lower bound method. Finally, simulation results demonstrate that the proposed STAR-RIS-enabled DP-FL systems achieve significantly improved secrecy performance compared to conventional FL systems with randomly configured STAR-RIS amplitude, phase shifts, and transmit power. Furthermore, the proposed FL algorithm achieves model training and testing performance that closely approximates that of FL without DP, highlighting its effectiveness in preserving both data privacy and model utility.

This paper investigates an energy-efficient beamforming design for a hardware-impaired simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided integrated sensing and communication (ISAC) system, where the base station (BS) concurrently performs target sensing and multi-user communication. Accounting for hardware impairments (HWIs) at the BS, user equipments (UEs) and STAR-RIS, a joint optimization problem is posed to maximize the system energy efficiency, subject to constraints on required sensing and transmission capabilities, and the total power budget. To tackle the intractable conflicts among sensing and transmission metrics introduced by HWIs, we propose a novel learning-based method that integrates a denoising diffusion probabilistic model (DDPM) into a twin-delayed deep deterministic policy gradient (TD3) algorithm enhanced with prioritized experience replay (PER). By leveraging the DDPM and PER for beamforming policy determination, our approach accurately models the complex dynamics, achieving a better balance between sensing and communication performance. Simulation results demonstrate that the proposed PER-DDPM-TD3-based beamforming strategy achieves a 69.3% higher energy efficiency performance than the existing deep reinforcement learning (DRL)-based method.

Hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks (SUTNs) can provide maritime users with ubiquitous communication services. However, eavesdropping poses a significant challenge to the secure communications of SUTNs due to their wide-area coverage. In this paper, we propose a novel secure scheme for maritime communications, where a terrestrial-UAV integrated network coexists with marine satellite (MS) systems in the presence of an eavesdropper (Eve). Considering imperfect channel state information (CSI) for both the MS and Eve, we focus on the collaborative design of beamforming for the terrestrial base station (TBS), UAV, and MS, as well as the UAV’s trajectory. A robust optimization problem is formulated to maximize the worst-case secrecy rate, subject to constraints on worst-case communication quality for each user, UAV locations, and TBS backhaul throughput. To tackle this intractable non-convex problem, we leverage the S-procedure, general sign-definiteness, and successive convex approximation (SCA) to propose a security solution that efficiently optimizes all variables using convex optimization techniques. Numerical results validate the effectiveness of the proposed solution, illustrating the impact of CSI errors and the secure performance enhancements achieved through joint trajectory and beamforming optimization.

In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM. This innovative approach leverages the additional degree of freedom (DoF) provided by the amplitude domain of the active RIS to perform index modulation (IM), thereby enhancing spectral efficiency (SE) without increasing the costs associated with additional radio frequency (RF) chains. Specifically, the ARIS-ADRM scheme transmits information bits through both the modulation symbol and the index of active RIS amplitude allocation patterns (AAPs). To evaluate the performance of the proposed ARIS-ADRM scheme, we provide an achievable rate analysis and derive a closed-form expression for the upper bound on the average bit error probability (ABEP). Furthermore, we formulate an optimization problem to construct the AAP codebook, aiming to minimize the ABEP. Simulation results demonstrate that the proposed scheme significantly improves error performance under the same SE conditions compared to its benchmarks. This improvement is due to its ability to flexibly adapt the transmission rate by fully exploiting the amplitude domain DoF provided by the active RIS.

The integration of aerial reconfigurable intelligent surfaces (ARISs) into IoT networks offers transformative potential for enhancing secure and energy-efficient communication in the presence of blockages and eavesdropping threats. This paper proposes to integrate ARIS into Internet of Things (IoT) networks to simultaneously improve communication reliability, enforce information freshness, and defend against eavesdropping. We formulate a joint optimization problem to minimize the average total transmit energy of IoT devices through the coordinated design of unmanned aerial vehicle (UAV) trajectory, transmit power allocation, ARIS phase shifts, and device scheduling, subject to rigorous constraints on age of information (AoI), UAV energy budget, and secrecy rate guarantees. The optimization problem is formulated as a dynamic programming problem. To address the complexity of long-term dynamic optimization, we employ Lyapunov optimization to decompose it into a per-slot deterministic optimization problem, which can be solved without requiring future state information. However, the per-slot problem is a mixed-integer non-convex optimization problem, making it inherently challenging to solve optimally. To address this, we propose an efficient algorithm that effectively balances the tradeoff between minimizing average total energy consumption and stabilizing average total queue backlogs. Simulation results demonstrate that our algorithm reduces average transmit energy by 46% compared to the round-robin comparison scheme while strictly adhering to information freshness and UAV energy constraints.

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resource consumption in wireless communications. To address these challenges, we propose a multi-layer hybrid bit and generative semantic communication framework which can adapt to the dynamic satellite communication networks. Furthermore, to balance the semantic communication efficiency and performance in satellite-to-ground transmissions, we introduce a novel semantic communication efficiency metric (SEM) that evaluates the trade-offs among latency, computational consumption, and semantic reconstruction quality in the proposed framework. Moreover, we utilize a novel deep reinforcement learning (DRL) algorithm group relative policy optimization (GRPO) to optimize the resource allocation in the proposed network. Simulation results demonstrate the flexibility of our proposed transmission framework and the effectiveness of the proposed metric SEM, illustrate the relationships among various semantic communication metrics.

This paper investigates joint transmit beamforming and trajectory optimization techniques for an unmanned aerial vehicle (UAV)-enabled covert full-duplex (FD) integrated sensing and communication (ISAC) systems with hardware impairments (HWIs), where the aerial access point (AP) transmits and receives sensing signals while the integrated communication operates in either downlink or uplink. We jointly optimize the downlink transmit signal and the uplink receive beamformers at the AP, the transmit power at the uplink users and the trajectory of the UAV. An optimization problem is formulated for maximizing the minimum covert transmission rate (CTR) among all covert users (CUs) subject to the constraints of the required sensing and uplink transmission capabilities, system covertness, total power budget. To tackle the intractable non-convex problem, we leverage majorization-minimization (MM) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Numerical results demonstrate the effectiveness of the proposed method in balancing the trade-off between covert communication and sensing performance, highlighting the UAV’s potential in adaptive ISAC deployment.

This paper proposes a novel shape index modulation (SIM) scheme for fluid antenna (FA) systems, termed as FA-SIM, exploiting the dynamic reconfigurability of FA shapes to introduce an additional index dimension for information encoding. By integrating SIM into FA systems, the proposed FA-SIM scheme enhances transmission efficiency and spectral utilization without increasing hardware complexity, offering improved flexibility and performance in next-generation wireless communications. Furthermore, we derive a closed-form expression for the upper bound on the average bit error probability (ABEP), providing theoretical insights into the system's error performance. Simulation results demonstrate that the proposed FA-SIM scheme achieves higher spectral efficiency (SE) than conventional fixed-position antenna systems while maintaining a cost-effective hardware implementation.

This article established a novel multi-input multioutput (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter, the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two subproblems. For the beamforming matrices optimization of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

In the online social networks (OSNs) and social Internet of Things (SIOT), communities of interest (CoI) are often used to facilitate information sharing among user devices. However, the risk of information leaks across communities persists due to inadequate control over users’ sharing behavior. In this paper, we propose a novel trust-based community sharing mechanism to control users who are contributing to high privacy leakage across communities of an OSN. In detail, we firstly formulate privacy loss in community based on the sensitivity and willingness of users in sharing. Secondly, we use this loss as the key determinant when updating trust to dynamically hold users accountable for privacy leakage. Thirdly, we use an adjustable threshold to enable or disable sharing users and evaluate the amount of information shared before and after control, as well as the changes in community user trust. Finally, we propose an optimization method based on the upper confidence bound to make a trade-off between information sharing and leakage through a payoff function over discretized thresholds. Simulations on three real OSNs datasets — BlogCatalog, Flickr, and YouTube — demonstrated that our proposed mechanism can effectively reduce community privacy loss by achieving the best payoff score of 1076.53, while the state-of-the-art baselines PDC-InfoSharing and UTV scored 506.33 and 957.98, respectively.

In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users. Considering the limitations in signal coverage and receiver antennas that make the direct communication between satellites and ground users unfeasible in many scenarios, thus UAVs serve as relays and forward images from satellites to the ground users. Our hybrid communication framework effectively combines bit-level transmission with several semantic-level image generation modes, optimizing bandwidth usage to meet stringent satellite link budget constraints and ensure communication reliability and low latency under low signal-to-noise ratio (SNR) conditions. To reduce the transmission delay while ensuring reconstruction quality for the ground user, we propose a novel metric to measure delay and reconstruction quality in the proposed system, and employ a deep reinforcement learning (DRL)-based strategy to optimize resource allocation in the proposed network. Simulation results demonstrate the superiority of the proposed framework in terms of communication resource conservation, reduced latency, and maintaining high image quality, significantly outperforming traditional solutions. Therefore, the proposed framework can ensure the real-time image transmission requirements in SAGINs, even under dynamic network conditions and user demand.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access scheme for enabling massive connectivity in next generation wireless networks. However, current SCMA codebooks are designed with the same size, leading to inflexibility of user grouping and supporting diverse data rates. To address this issue, we propose a variable modulation SCMA (VM-SCMA) that allows users to employ codebooks with different modulation orders. To guide the VM-SCMA design, a VM matrix (VMM) that assigns modulation orders based on the SCMA factor graph is first introduced. We formulate the VM-SCMA design using the proposed average inverse product distance and the asymptotic upper bound of sum-rate, and jointly optimize the VMM, VM codebooks, power and codebook allocations. The proposed VM-SCMA not only enables diverse date rates but also supports different modulation order combinations for each rate. Leveraging these distinct advantages, we further propose an adaptive VM-SCMA (AVM-SCMA) scheme which adaptively selects the rate and the corresponding VM codebooks to adapt to the users’ channel conditions by maximizing the proposed effective throughput. Simulation results show that the overall designs are able to simultaneously achieve a high-level system flexibility, enhanced error rate results, and significantly improved throughput performance, when compared to conventional SCMA schemes.

This paper presents a general framework for probabilistic relay selection (RS) in asymmetric buffer-aided cooperative relaying systems, which caters to scenarios with both perfect and imperfect channel state information (CSI) during the RS process. The framework extends and generalizes many existing buffer-aided RS schemes. In particular, we introduce an auxiliary stochastic process which assigns varying selection probabilities to different links, considering the dynamic wireless channel and buffer states. Subsequently, we leverage the obtained outage probability and average packet delay (APD) to formulate outage optimization problems while adhering to APD. To address the intricate high-dimensional optimization problems, we employ a deep learning (DL) approach, which involves designing probability mass functions for the auxiliary stochastic process and developing an effective loss function to update the neural network. Simulation results unequivocally demonstrate the superior performance of the proposed DL-based probabilistic RS scheme compared to benchmark schemes, particularly in scenarios involving imperfect CSI.

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known channel state information (CSI) for the eavesdropping target, we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the discrete reflection coefficients (DRC) of the multiple hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

In this paper, we investigate hybrid passive and active intelligent reflecting surface (IRS)-assisted secure non-orthogonal multiple access (NOMA) networks. Multiple users concurrently transmit sensitive data to an access point (AP) in the presence of an eavesdropper (Eve). The hybrid IRS is employed to enhance the NOMA users’ sum rates while simultaneously performing jamming beamforming against the Eve by optimizing the communication channels of NOMA users and injecting controllable noise into the Eve’s channel. We formulate a sum secrecy rate maximization problem by jointly optimizing the users’ scheduling policy, the hybrid IRS’s working mode and beamforming, and the AP’s receiving beamforming. To address combinatorial user scheduling and high-dimensional beamforming design, we develop a dual-cycling deep reinforcement learning (DRL) framework. We first determine the NOMA users’ scheduling strategy and the hybrid IRS’s working mode using a proximal policy optimization (PPO)-based learning algorithm. Then, we optimize the AP’s receiving beamforming and hybrid IRS’s beamforming strategies using an alternating optimization (AO) algorithm. The joint beamforming optimization can significantly enhance the DRL’s learning efficiency by limiting its action space. Moreover, we propose a lightweight two-phase algorithm with approximation techniques to reduce computational complexity by eliminating double-nested loops in AO, while maintaining secrecy performance close to optimum. Numerical results demonstrate that the proposed dual-cycling DRL scheme achieves 54.85% gains in the secrecy rate compared to traditional DRL schemes.

Distributed reconfigurable intelligent surfaces (RISs) provide rich macro-diversity coverage due to different locations of the RISs, which is beneficial to combat coverage holes. However, the system performance relies on the effective coordination of multiple RISs. In particular, distributed RIS-assisted power allocation and the phase shifts of RISs should be jointly designed under nonlinear scheduling constraints. Thus, the resource allocation scheme for distributed RIS-assisted multiuser system is a crucial challenge. To tackle these issues, joint power allocation, phase shifts and communication scheduling design for distributed RIS-assisted systems is investigated in this paper, where all RISs simultaneously and cooperatively serve multiple users. To overcome the formulated nonconvex optimization problem, the original problem is decoupled into three subproblems and solved in an iterative manner. Specifically, we first consider the subproblem of power allocation, which can be solved via maximizing the ergodic achievable rate. By applying the ergodic rate, an approximate closed-form solution is formed for the power allocation. Subsequently, the phase shifts are optimized using the minimization-maximization optimization methods. Finally, a communication scheduling scheme is presented to address the scheduling variables. Numerical simulations are conducted to demonstrate that the considered solution outperforms the existing benchmark and achieves a near-optimal spectral efficiency.

Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations.

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS.

Traditional self-interference cancellation (SIC) methods are common in full-duplex (FD) integrated sensing and communication (ISAC) systems. However, exploring new SIC schemes is important due to the limitations of traditional approaches. With the challenging limitations of traditional SIC approaches, this paper proposes a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled FD ISAC system, where STAR-RIS enhances simultaneous communication and target sensing and reduces self-interference (SI) to a level comparable to traditional SIC approaches. The optimization of maximizing the sensing signal-to-interference-plus-noise ratio (SINR) and the communication sum rate, both crucial for improving sensing accuracy and overall communication performance, presents significant challenges due to the non-convex nature of these problems. Therefore, we develop alternating optimization algorithms to iteratively tackle these problems. Specifically, we devise the semi-definite relaxation (SDR)-based algorithm for transmit beamformer design. For the reflecting and refracting coefficients design, we adopt the successive convex approximation (SCA) method and implement the SDR-based algorithm to tackle the quartic and quadratic constraints. Simulation results validate the effectiveness of the proposed algorithms and show that the proposed deployment can achieve better performance than that of the benchmark using the traditional SIC approach without STAR-RIS deployment.

In this paper, we propose a novel composition aided generalized quadrature spatial modulation (C-GQSM) scheme to improve the spectral efficiency (SE) of the GQSM systems by exploiting the power domain degree of freedom. The C-GQSM scheme constitutes a hybridization of GQSM and composition modulation (CM) principles, allowing the information bits to encompass not only the antenna activation patterns (AAPs) and amplitude/phase modulated (APM) constellation symbols, but also the energy allocation patterns (EAPs). In addition, we present two low-complexity detection techniques for the proposed C-GQSM system. The first one is based on the ordered successive interference cancellation (OSIC) technique, while the other based on the weighted coordinate descent (WCD) algorithm. Moreover, the upper bound of the average bit error probability (ABEP) of the proposed C-GQSM scheme is derived under both uncorrelated and correlated channel conditions. Simulation results show that the proposed C-GQSM outperforms both the conventional CM and GQSM systems in terms of SE without sacrificing the bit error rate (BER) performance.

In this paper, we propose a fluid antenna (FA) enabled joint transmit and receive index modulation (FA-JTR-IM) transmission mechanism for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems. By integrating the methodologies of FA and IM, the proposed scheme achieves enhanced spectral efficiency (SE) while requiring only a single radio frequency (RF) chain at both the transmitter and receiver. The proposed scheme offers a low hardware cost and power consumption transmission mechanism for the RIS-aided mmWave communication systems. Specifically, the encoding of information bits encompasses not only the modulated symbol but also the indices of transmit FA positions and receive antennas. To achieve a reliability-complexity trade-off, two types of detectors are introduced for the proposed FA-JTR-IM scheme, including the optimal maximum likelihood (ML) detector and two-step sequential (TSS) detector. Based on the ML detector, we derive the expression for the conditional pair-wise error probability of the proposed FA-JTR-IM scheme. Additionally, we provide the closed-form expressions for the unconditional PEP under the finite-path and infinite-path channel conditions, respectively. Simulation results demonstrate the superiority of the proposed FA-JTR-IM scheme in terms of error performance over its conventional benchmark schemes under the same SE condition.

Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks. This paper proposes a single sparse graph (SSG) enhanced expectation propagation algorithm (EPA) receiver, referred to as SSG-EPA, for uplink MIMO-SCMA systems. Firstly, we reformulate the sparse codebook mapping process using a linear encoding model, which transforms the variable nodes (VNs) of SCMA from symbol-level to bit-level VNs. Such transformation facilitates the integration of the VNs of SCMA and low-density parity-check (LDPC), thereby emerging the SCMA and LDPC graphs into a SSG. Subsequently, to further reduce the detection complexity, the message propagation between SCMA VNs and function nodes (FNs) are designed based on EPA principles. Different from the existing iterative detection and decoding (IDD) structure, the proposed EPA-SSG allows a simultaneously detection and decoding at each iteration, and eliminates the use of interleavers, de-interleavers, symbol-to-bit, and bit-to-symbol LLR transformations. Simulation results show that the proposed SSG-EPA achieves better error rate performance compared to the state-of-the-art schemes.

This paper investigates the amalgamation of affine frequency division multiplexing (AFDM) with sparse code multiple access (SCMA), termed as AFDM-SCMA, to facilitate massive connectivity in high-mobility scenarios. We start by introducing the basic principles of SCMA and AFDM systems and then present the proposed AFDM-SCMA system with multiple input and multiple output (MIMO) for both downlink and uplink channels. A two stage detector is proposed for the multi-user detection of the downlink channels. Additionally, to reduce the detection complexity and exploit the channel sparsity, we propose an expectation propagation algorithm (EPA)-aided low complexity receiver for uplink channels. Through numerical simulations, we validate the enhanced performance of the proposed AFDM-SCMA systems compared to conventional orthogonal frequency division multiplexing-empowered SCMA (OFDM-SCMA) systems in terms of error rate performance.

Ultra-massive multiple-input multiple-output (UM-MIMO) has been considered as one of the promising technologies for terahertz (THz) wireless communications to compensate for the severe path loss. However, the widely acknowledged hybrid beamforming approaches in massive MIMO cannot deal with the beam split effect caused by the increased scale of array dimension and system bandwidth in THz UM-MIMO systems. In this paper, the hybrid beamforming specifically designed for wideband THz UM-MIMO systems with the beam split effect is proposed. Firstly, a novel technique of hybrid beamforming is formulated as a sub-beam coherent combination method to cover the dispersed spatial directions. Subsequently, the dynamic hybrid hardware architecture is considered to flexibly adapt to various beam splits on different path directions, in which the optimal number of the activated subarray is elaborately designed to alleviate beam split while reducing power consumption. Simulation results indicate that our scheme achieves desirable beamforming gain distribution across the entire bandwidth, as well as achieving higher energy efficiency than other fixed hybrid hardware architectures designed for alleviating the beam split effect.

This paper investigates the cooperative jamming based key generation scheme in a cooperative wireless network, where Alice and Bob aim to generate a secret key (SK) that is secret from Eve and a private key (PK) that is secret from both the relay and Eve, with the assistance of a relay. The correlation between legitimate channels and eavesdropping channels is considered. The SK and PK rates are evaluated based on minimum mean square error (MMSE) and zero forcing (ZF) estimation methods, respectively. The analytical expressions are further simplified with asymptotic forms in the high signal-to-noise ratio (SNR) regime. We demonstrate that the MMSE method is optimal to estimate channels, and the ZF method is asymptotically optimal in the high SNR regime. Moreover, we prove that the double-hop key generation strategy cannot achieve a higher rate than the single-hop strategy. Finally, numerical results validate the analysis and demonstrate that the cooperative jamming based key generation scheme significantly outperforms the traditional pairwise key generation scheme.

In this paper, we present a double‐intelligent reflecting surfaces (IRS)‐assisted multiuser secure system where the inter‐IRS channel is considered. In particular, we maximize the weighted sum secrecy rate of the system by jointly optimizing the beamforming vector for transmitted signal and artificial noise at the base station (BS) and the cooperative phase shifts of two IRSs, under the constraints of transmission power at the BS and the unit‐modulus phase shift of IRSs. To tackle the nonconvexity of the optimization problem, we first convert the objective function to its concave lower bound by utilizing a novel successive convex approximation technique, then solve the transformed problem iteratively by applying an alternating optimization method. The Lagrange dual method, Karush–Kuhn–Tucker conditions, and alternating direction method of multipliers are applied to develop a low‐complexity solution for each subproblem. Finally, simulation results are provided to verify the advantages of the cooperative double‐IRS scheme in comparison with the benchmark schemes.

Low-utilized antenna subarrays can be switched to sleep mode during traffic valleys to save energy. However, when an antenna subarray enters sleep mode, beam services connected with this subarray must be reassociated with another subarray, inevitably degrading the Quality of Service (QoS) for the beams. In this article, we investigate an energy-efficient and QoS guaranteed 3-D beam mapping problem for 2-D antenna subarray selection and radio resource block (RB) allocation in massive multiple input–multiple output (MIMO) systems under a tidal traffic load. The 3-D beam mapping problem is formulated as a mixed integer linear programming (ILP) model to find the optimal solution. To address the scalability issue of the ILP model, we propose a load adjustment (LA) with greedy searching (LA-GS) algorithm to optimize both the energy consumption (EC) of antenna subarrays and the traffic migration of beam services. Moreover, two benchmark algorithms, load reallocation (LR) and LA, are designed for performance comparison. Extensive numerical results demonstrate that the proposed LA-GS algorithm can guarantee both low EC and minimal traffic migration. Compared with the designed LR algorithm, our proposed LA-GS method can achieve up to a 28.3% cost reduction, primarily attributed to a 43.1% reduction in traffic migration with at most 1.9% higher EC.

The space-air-ground integrated network (SAGIN) has become a crucial research direction in future wireless communications due to its ubiquitous coverage, rapid and flexible deployment, and multi-layer cooperation capabilities. However, integrating hierarchical federated learning (HFL) with edge computing and SAGINs remains a complex open issue to be resolved. This paper proposes a novel framework for applying HFL in SAGINs, utilizing aerial platforms and low Earth orbit (LEO) satellites as edge servers and cloud servers, respectively, to provide multi-layer aggregation capabilities for HFL. The proposed system also considers the presence of inter-satellite links (ISLs), enabling satellites to exchange federated learning models with each other. Furthermore, we consider multiple different computational tasks that need to be completed within a limited satellite service time. To maximize the convergence performance of all tasks while ensuring fairness, we propose the use of the distributional soft-actor-critic (DSAC) algorithm to optimize resource allocation in the SAGIN and aggregation weights in HFL. Moreover, we address the efficiency issue of hybrid action spaces in deep reinforcement learning (DRL) through a decoupling and recoupling approach, and design a new dynamic adjusting reward function to ensure fairness among multiple tasks in federated learning. Simulation results demonstrate the superiority of our proposed algorithm, consistently outperforming baseline approaches and offering a promising solution for addressing highly complex optimization problems in SAGINs.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

User-centric (UC) philosophy is a promising network formation method in light emitting diode enabled visible light communication (VLC) systems. Nevertheless, the nonlinear channel impairments restrict the overall system performance and have not been fully considered in the association structure designing. In this paper, an adaptive user association approach within the UC-cells formation of dense VLC networks is investigated under the consideration of practical nonlinear impairments and adjacent interference. It is mathematically formulated to be an achievable data rate maximization problem by coordinately determining the optimal candidates of access point, clipping ratio and information-carrying power. We divide this mixed combinatorial and non-convex optimization problem into two subproblems and delicately transform them to be binary nonlinear programming and constrained linear programming problems, respectively. In addition, we develop an efficient approach to obtain the local optimal solution with low-computational complexity in an alternating iterative way. Simulation results demonstrate that the proposed scheme has relatively fast convergence and shows robustness to the variation of complex interference patterns and nonlinear impairments. Moreover, it can achieve significant throughput gain as compared with the conventional schemes, demonstrating the prospect and validity of this methodology for dense VLC networks with actual nonlinear devices.

Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ES-RIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional self-interference cancellation technology.

In this paper, we propose a secure unmanned aerial vehicle (UAV) communication system with the assistance of a reconfigurable intelligent surface (RIS), where UAV trajectory design and artificial noise are incorporated to prevent eavesdropping from multiple colluding curious users. To maximize the secrecy rate of the proposed system, we undertake a joint optimization process that encompasses the trajectory of the UAV, the RIS phase shifts, and the beamforming vectors for both information and artificial noise signals, considering the constraints of the UAV transmit power, UAV flying speed and the phase shifts. To address the non-convex nature of the joint problem and handle the coupling effects of multiple parameters, we conduct the problem decomposition by using the block coordinate descent (BCD) method, combined with an alternating algorithm to optimize the decomposed sub-problems. To further tackle the non-convexity in sub-problems, we apply the successive convex approximation (SCA) method to circumvent the trajectory optimization problem and to optimize the beamformers of information and artificial noise signals, while a majorization-minimization (MM) based scheme is adopted for the RIS phase shifts optimization. Numerical simulation results substantiate the convergence and effectiveness of the proposed algorithm through the comparison with benchmark methods, and our proposed scheme is proven to achieve a significant improvement in average secrecy rate across various conditions.

This paper investigates the problem of secure transmission over a two-user discrete memoryless multiple-access wiretap channel with partial rate-limited feedback (MAC-WT-PLF). The receiver can causally and securely transmit feedback to one of the transmitters at a limited rate. Three achievable rate regions and one outer bound on the secrecy capacity are presented based on three proposed coding schemes and the Sato-type bounding approach. The proposed coding schemes show that the feedback can play multiple roles, i.e., encrypting part of messages, enlarging the size of the dummy message, and increasing the correlation between the channel inputs, to enhance the secrecy performance. Of particular interest is identifying the novel role of enlarging the size of the dummy message at one of the transmitters, which enables both transmitters to benefit from the feedback significantly. In addition, the proposed achievable rate regions and outer bound are computed for the Gaussian MAC-WT-PLF, and comparative numerical results are provided under different eavesdropping cases.

This paper investigates the performance of key generation between two nodes assisted by a relay in the presence of correlated eavesdropping channels. A cooperative jamming scheme is utilized to impose superimposed channel measurements on the relay and eavesdropper. Both lower and upper bounds on key capacities for both secret key (SK) and private key (PK) generation are evaluated, where the lower bounds are derived by using minimum mean square error and zero forcing methods for channel estimation, and the upper bounds are derived by formulating several enhanced discrete memoryless source (DMS) models. The analytical expressions are further simplified in the high signal-to-noise ratio (SNR) regime. We discover that one of the two legitimate channels should specialize in playing a role of jamming the relay or eavesdropper. We also demonstrate that the derived lower and upper bounds are tight when the eavesdropping channels are lowly or highly correlated. When the eavesdropping channels are uncorrelated, the SK and PK capacities can be determined since the corresponding upper and lower bounds are equal. Moreover, at high SNRs, a constant gap exists between the SK/PK upper and lower bounds as the correlation coefficient becomes one.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms’ efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

This work presents a novel K-Repetition based Hybrid Automatic Repeat reQuest (HARQ) scheme for uplink sparse code multiple access (SCMA) systems. Our core idea is to apply network coding (NC) principle to re-encode different packets (after channel coding and interleaving) or their fragments, where K-Repetition is an emerging HARQ technique (recommended in 3GPP Release 15) for enhanced reception in future massive machine-type communications. Such a proposed scheme is referred to as the NC aided K-repetition SCMA (NCK-SCMA) in this paper. We aim to understand the optimal NCK-SCMA design criteria for maximizing the channel diversity as well as the efficient receiver processing for superior error rate performances. It is found that NC can enable a larger diversity order for NCK-SCMA with fewer resources (i.e., higher spectrum efficiency). Toward this objective, some novel design criteria are developed for the efficient configuration of NCK-SCMA. Moreover, we propose an iterative network decoding and SCMA detection (INDSD) algorithm for robust and low-complexity recovery of the transmit data from a low-density parity-check (LDPC) coded uplink NCK-SCMA system. Simulation results demonstrate that the proposed NCK-SCMA lead to higher throughput and improved reliability over the conventional K-SCMA.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for the cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS, but the performance improvement is at the cost of the requirement on the elements of UAV-RIS.

Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems.

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UM-MIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios.

In this paper, we investigate the fairness of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) for eliminating the interference of the sensing signal before decoding the signals of communication users. We formulate the problem of maximizing the fairness between communication users and the sensing target by jointly designing the transmit beamforming vectors of the base station (BS) and the coefficient matrices of the STAR-RIS. For tackling the challenging optimization problem, a low-complexity algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP) techniques is proposed for obtaining the transmit beamforming vectors and the STAR-RIS coefficient matrices. For the ISAC system with a single user, we further derive the closed-form expression of the BS transmit beamforming vector for reducing the complexity of the algorithm. Then, the non-convex optimization problem of the STAR-RIS coefficient matrices can be solved efficiently by transforming it into a convex problem. Simulation results show that the fairness of the proposed STAR-RIS-NOMA assisted ISAC system outperforms the conventional RIS-NOMA assisted ISAC system and the conventional RIS and orthogonal multiple access (RIS-OMA) assisted ISAC system.

This paper considers reconfigurable intelligent surface (RIS)-enabled federated learning (FL) system, where the FL users communicate with the access point (AP) via RIS. To reveal the impact of RIS and learning rate on FL aggregation, the theoretical result of minimum global communication rounds and local iteration rounds are derived. Based on the obtained convergence results of FL, we formulate an optimization problem to minimize the energy consumption of the proposed RIS-assisted FL system by jointly optimizing the passive beamforming of RIS, the CPU computing frequency, the bandwidth, and the transmit power of users. To solve the non-convex problem, we propose a block coordinate descent (BCD) optimization algorithm based on successive convex approximation (SCA) to decompose the original problem into four sub-problems. Specifically, the closed-form solutions are derived for the CPU frequency, RIS reflection matrix, and communication bandwidth. For the transmit power sub-problem, we propose a linear approximation algorithm based on the first-order Taylor expansion to ensure solution accuracy. Finally, simulation results show that: 1) the energy consumption of the proposed RIS-assisted FL system can be greatly reduced compared to that without optimizing the passive beamforming of RIS and the transmit power; 2) The learning performance of the proposed RIS-enabled FL system is closed to the FL without wireless communication interference; and 3) The proposed algorithm can not only significantly reduce energy consumption, but also fast convergence in terms of the FL model training and testing.

Most of the existing research on degrees-of-freedom (DoF) with imperfect channel state information at the transmitter (CSIT) assume the messages are private, which may not reflect reality as the two receivers can request the same content. To overcome this limitation, we therefore consider the hybrid unicast and multicast messages. In particular, we characterize the optimal DoF region for the two-user multiple-input multiple-output (MIMO) broadcast channel (BC) with imperfect CSIT and hybrid messages. For the converse, we establish a three-step procedure to exploit the utmost possible relaxation. For the achievability, since the DoF region is with specific three-dimensional structure regarding antenna configurations and CSIT qualities, we verify the existence or non-existence of corner point candidates via the feature of antenna configurations and CSIT qualities categorization, and provide a hybrid message-aware rate-splitting scheme. Besides, we show that to achieve the strictly positive corner points, it is unnecessary to split the unicast messages into private and common parts. This implies adding a multicast message may mitigate the rate-splitting complexity.

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients, and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

In MIMO multi-user networks, inter-user interference (IUI) significantly affects the system performance. To handle this problem, this paper proposes the reconfigurable intelligent surface assisted cooperative interference alignment scheme (RIS-CIA). The core idea of this work is that the base station and full-duplex users jointly design space-time precoding matrices, which can reduce the dimension of the interference space on the user side. Besides, the additional interference caused by the information exchange process is split into sub-blocks by space-time precoding, then eliminated by interference nulling assisting by the passive RIS. The simulation results show that the RIS-CIA scheme with few numbers of elements obtains higher DoF than that of benchmark schemes with a huge number of elements.

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

In this paper, a novel index and composition modulation (ICM) transmission scheme, termed as grouped generalized composition and spatial modulation (G-GCSM), is proposed for massive multiple-input multiple-output (MIMO) systems. Specifically, it amalgamates the concepts of composition modulation (CM), generalized spatial modulation (GSM) and spatial multiplexing to attain high spectral efficiency (SE) and low implementation complexity. In the G-GCSM scheme, transmit antennas are divided into several groups and the GCSM transmission structure is employed independently in each group, facilitating the bit-to-index mapping issue in massive MIMO scenarios. Additionally, at the receiver side, an improved expectation propagation (EP) detector is designed for the proposed G-GCSM scheme, which exploits the inner sparsity of the transmitted vector in G-GCSM. Simulation results demonstrate the superiority of the proposed scheme over the existing GSM schemes in terms of bit error rate (BER) performance under the same SE conditions. Moreover, the proposed improved EP detector is able to provide a significant performance gain over the conventional minimum-mean-squared error (MMSE) detector in both determined and under-determined massive MIMO systems.

Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary features (e.g., fields of views, communication links, computing capabilities), a promising paradigm termed integrated robotics network therefore emerges, which provides communications for cooperative UAVs-UGVs applications. However, how to efficiently deploy UAVs and schedule the UAVs-UGVs connections according to different UGV tasks become challenging. In this paper, we consider the sum-rate maximization problem, where UGVs plan their trajectories autonomously and are dynamically associated with UAVs according to their planned trajectories. Although this problem is a NP-hard mixed integer program, a fast polynomial time algorithm using alternating gradient descent and penalty-based binary relaxation, is devised. Simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a rate-splitting design and characterize the sum-degrees-of-freedom (DoF) for the K-user multiple-input-single-output (MISO) broadcast channel with mixed channel state information at the transmitter (CSIT) and order-(K − 1) messages, where mixed CSIT refers to the delayed and imperfect-current CSIT, and order-(K − 1) message refers to the message desired by K − 1 users simultaneously. In particular, for the sum-DoF lower bound, we propose a rate-splitting scheme embedding with retrospective interference alignment. In addition, we propose a matching sum-DoF upper bound via genie signalings and extremal inequality. Opposed to existing works for K = 2, our results show that the sum-DoF is saturated with CSIT quality when CSIT quality thresholds are satisfied for K > 2.

Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance, link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem, a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance.

Abstract Channel state information (CSI), which is crucial for resource allocation and system performance in time division duplex (TDD) massive multiple‐input multiple‐output (MIMO) systems, is difficult to predict because of the channel's time‐varying nature. To overcome this limitation, a scheme for channel prediction combined with deep learning (DL) is proposed. The system uses a deep neural network (DNN) to interpolate channel estimates from a few received pilot signals and a recurrent neural network (RNN) to train through the current time and the recent historical channel estimates to predict the CSI while the channel is constantly varying. In the end, a mixed neural network of RNN and DNN, is called MRDNN. In addition, the proposed DL‐based method does not rely on the relevant feature information about the channel, such as internal characteristics and parameters of the channel itself or channel statistical information, which improves its effectiveness in practical applications. The results of the simulation show that the MRDNN‐based method is better than the existing methods, like traditional AR method and NL Kalman method, and also can be effective in improving the quality of channel prediction and the performance of the system under the dynamic change scenario of low mobility.

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search.

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS’s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available athttps://github.com/ethanlq/SCMA-codebook.

In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted large-scale network by characterizing the coverage probability and the average achievable rate using stochastic geometry. Considering the spatial correlation between transmitters (TXs) and RISs, their locations are jointly modelled by a Gauss-Poisson process (GPP). Two association strategies, i.e., nearest association and fixed association, are both discussed. For the RIS-aided transmission, the signal power distribution with a direct link is approximated by a gamma random variable using a moment matching method, and the Laplace transform of the aggregate interference power is derived in closed form. Based on these expressions, we analyze the channel hardening effect in the RIS-assisted transmission, the coverage probability, and the average achievable rate of the typical user. We derive the coverage probability expressions for the fixed association strategy and the nearest association strategy in an interference-limited scenario in closed form. Numerical results are provided to validate the analysis and illustrate the effectiveness of RIS-assisted transmission with passive beamforming in improving the system performance. Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.

Buffer-aided relay networks provide more reliability and coverage in future wireless communications. Therefore, this paper investigates a buffer-aided cooperative relaying system with$K$relays and a direct link from the source to the destination, providing a general scenario different from other existing state-of-the-art techniques. In particular, we propose a novel link selection scheme, which adaptively coordinates the selection priorities of the direct and cooperative relay link according to the instantaneous buffer state. The performance of the proposed link selection scheme is analyzed, in terms of outage probability, average packet delay (APD) and diversity order by providing closed-form expressions. For asymptotic analysis, a theoretical framework is presented by dividing all buffer states into different sets, which verifies that the minimum buffer size is just two for achieving the full diversity order of$2K+1$. We also provide the relationship between the asymptotic APD and diversity order by adjusting predefined target queue lengths, which shows that the diversity order ranges from$K+1$to$2K+1$as the asymptotic APD ranges from 0 to$K$time slots per packet. Both theoretical and simulation results demonstrate that direct transmission significantly improves the outage and delay performance simultaneously.

In fifth generation networks (5G), beamforming technique is widely used to obtain higher system capacity, but it cannot eliminate inter-user interference (IUI) of networks due to excessive number of users. To handle this problem, interference alignment (IA) schemes attract great attention as they can effectively restrain IUI. However, the existing IA schemes cannot achieve antenna adaptation and the obtained degree of freedom (DoF) may be not optimal. In this paper, a novel antenna adaptation based interference elimination and regeneration (AA-IER) scheme is proposed for cooperative networks, where a relay with hybrid antenna array structure is adopted to assist the communication. The proposed transmission process is completed in two phases, including interference elimination phase (IEP) and interference regeneration phase (IRP). For the former, the IUI is eliminated and the redundant symbols are erased so that the received signal of multiple users can be decoded simultaneously. For the latter, the redundant symbols of all users are regenerated where the space resources are fully utilized. The simulation results show that AA-IER scheme obtains higher DoF than that of three benchmark schemes. Meanwhile, it requires fewer antennas of relay than HAA-CIE-RIA scheme.

This paper proposes a secure transmission in reconfigurable intelligent surfaces (RIS) aided non-terrestrial cooperative networks (NTCN), where the practical phase-dependent model is considered in which the RIS reflection amplitudes change with the corresponding discrete phase shifts. Moreover, we employ a full-duplex transmission scheme at the relay nodes to reduce the long-range signal loss and improve the security between the satellite and the relay node. To solve the complex nonconvex optimization problem of the joint RIS reflection coefficient and relay selection optimization, we propose the deep cascade correlation learning (DCCL) algorithm to enhance optimization efficiency. Simulation results show that the proposed DCCL-based method significantly improves the secrecy capacity compared to the random relay selection and RIS coefficient methods.

In this paper, we study the simultaneous wireless information and power transfer (SWIPT) cooperative system, where one source forwards information to one destination with the assistance of multiple relays. Each relay is equipped with a finite data butter and a finite energy butter storing the harvested energy by radio-frequency (RF). An optimization problem is formulated for throughput maximization of the SWIPT cooperative system, taking into consideration the strict delay constraint, dynamic channel conditions, time-varying discrete data butter states and time-varying continuous energy butter states. A discrete-time Markov decision process (MDP) is adopted to model the relay selection process referring to data butter states and energy butter states. Two deep Q-network (DQN)based methods named invalid action penalty (IAP) and invalid action mask (IAM) are proposed. The simulation results show that the proposed IAM method can achieve better convergence and throughput performance than the IAP method.

In this paper, we analyze the coverage probability of a reconfigurable intelligent surface (RIS) aided cellular network with the theory of stochastic geometry. A Poisson cluster process (PCP) is applied to model the positions of transmitters (TXs) and RISs, capturing their spatial correlations. Considering the general Nakagami-m fading channel model, we derive the approximate distributions of the composite channel gains with RIS-assisted transmission, representing the desired signal channel and the interference channel, respectively. The coverage probability of the typical user is then obtained. The derived coverage probability is in a closed form, which can be evaluated efficiently. Simulation results are presented to show that the presented analysis is effective, demonstrate the significant performance gains brought by the passive beamforming of a RIS with a large number of elements, and show the impact of TX density on the performance of the proposed system.

This article proposes a novel machine-learning-based routing optimization for the multiple reconfigurable intelligent surfaces (M-RIS)-assisted multihop cooperative networks, in which a practical phase model for reconfigurable intelligent surface (RIS) with the amplitude variation based on the corresponding discrete phase shift is considered. We aim to maximize the end-to-end data rate in the proposed network by jointly optimizing the data transmission path, the passive beamforming design of RIS, and transmit power allocation. To tackle this complicated nonconvex problem, we divide it into two subtasks: 1) the passive beamforming design of the RIS and 2) joint routing and power allocation optimization. First, for the passive beamforming design of RIS, we develop a distributed learning algorithm that employs a cascade forward backpropagation network in each relay node to solve the RIS coefficients optimization problem by directly using the optimization target to train the cascade networks. This solution can avoid the curse of dimensionality of traditional reinforcement learning algorithms in the RIS optimization problem. Then, based on the result of RIS optimization, we introduce the proximal policy optimization (PPO) algorithm with the clipping method to find solutions for joint optimization of routing and power allocation via achieving the long-term benefit in the Markov decision process (MDP). Simulation results show that the proposed learning-based scheme can learn from the environment to improve its policy stability and efficiency in the iterative training process for optimizing routing and RIS and significantly outperform the benchmark schemes.

This article proposes a wireless key generation solution for secure low-latency communications with active jamming attack prevention in wireless networked control systems (WNCSs) of Industrial Internet of Things (IIoT) applications. We first identify a new vulnerability in physical-layer key generation schemes using wireless channel and random pilots (RPs) in static environments. We derive a closed-form expression for the probability that the RP-based key is successfully attacked by a long-term eavesdropper at a fixed location. To prevent such attacks, we propose a one-time pad (OTP) encrypted transmission solution assisted by one-way self-interference (SI), which has low-latency, high-security benefits, and active attack detection capability. The performance of the proposed scheme is analytically compared with two benchmark RP-based schemes, and its advantages are verified in a ray-tracing-based simulation environment. We further investigate the impact of critical design parameters, which reveal fundamental insights for the deployment and implementation of our proposed secure communications scheme.

Grant-free sparse code multiple access (GF-SCMA) is considered to be a promising multiple access candidate for future wireless networks. In this article, we focus on characterizing the performance of uplink GF-SCMA schemes in a network with ubiquitous connections, such as the Internet-of-Things (IoT) networks. To provide a tractable approach to evaluate the performance of GF-SCMA, we first develop a theoretical model taking into account the property of multiuser detection (MUD) in the SCMA system. Then, the error rate performance of GF-SCMA in the case of codebook collision is analyzed to investigate the reliability of GF-SCMA when reusing codebook in massive IoT networks. For performance evaluation, accurate approximations for both success probability and average symbol error probability (ASEP) are derived. To elaborate further, the analytical results are utilized to discuss the impact of codeword sparse degree in GF-SCMA. After that, we conduct a comparative study between SCMA and its variant, dense code multiple access (DCMA), with GF transmission to offer insights into the effectiveness of these two schemes. This facilitates the GF-SCMA system design in practical implementation. Simulation results show that denser codebooks can help to support more user equipments (UEs) and increase the reliability of data transmission in a GF-SCMA network. Moreover, a higher success probability can be achieved by GF-SCMA with denser UE deployment at low detection thresholds since SCMA can achieve overloading gain.

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE.

This paper investigates joint transmit beampattern and phase shifts optimization techniques for a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) radar in the presence of an eavesdropping target. We propose an optimization technique to maximize the signal-to-interference plus noise ratio (SINR) at the MIMO radar. However, the problem is non-convex due to the non-concavity of the secrecy rate function. To tackle this issue, we apply the block coordinate descent (BCD) algorithm to update the transmit power and the phase shifts of the RIS alternately. Specifically, we utilize the majorization-minimization (MM) algorithm to optimize the phase shifts for a given transmit power and utilize the first-order Taylor expansion to reformulate the problem as a convex problem to optimize the transmit power for a given set of phase shifts. Two transmit beamforming vectors are designed to detect the target and convey information safely to the legitimate receiver. Simulation results show that the RIS-assisted MIMO radar can significantly enhance the SINR compared to an ordinary MIMO radar.

Unmanned aerial vehicle (UAV) will be an essential carrier for future wireless communications due to its flexible deployment and low cost. As such, the information security of UAV communications is of paramount concern. In this paper, a novel physical layer secret key generation scheme is proposed for air-to-ground (A2G) UAV multiple-input-multiple-output (MIMO) communications, which is applicable in frequency division duplex (FDD) systems. In UAV communications, line-of-sight (LoS) propagation is a distinctive feature, which significantly weakens the performance of channel state information (CSI) based keys. Therefore, a novel channel parameter, three-dimension (3D) spatial angle, is employed to combat against a novel active eavesdropping method, which is termed as Environment Reconstruction based Attack for SEcret keys (ERASE). Compared to the existing plane-angle-based method, our scheme can efficiently utilize spatial resources and provide a higher key generation rate (KGR). The advantages of the proposed scheme are shown through both theoretical analysis and simulations.

This article investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks. The buffer-aided relay selection significantly improves the outage performance but often at the price of higher latency. On the other hand, modern communication systems such as the Internet of Things often have strict requirement on latency. It is thus necessary to find relay selection policies to achieve good throughput performance in the buffer-aided relay network while stratifying the delay constraint. With the buffers employed at the relays and delay constraints imposed on the data transmission, obtaining the best relay selection becomes a complicated high-dimensional problem, making it hard for the reinforcement learning to converge. In this article, we propose the novel decision-assisted deep reinforcement learning to improve the convergence. This is achieved by exploring the a priori information from the buffer-aided relay system. The proposed approaches can achieve high throughput subject to delay constraints. Extensive simulation results are provided to verify the proposed algorithms.

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

The high spectrum efficiency of nonorthogonal multiple access (NOMA) is attractive to solve the massive number of connections in the Internet of Things (IoT). This article investigates a buffer-aided cooperative NOMA (C-NOMA) system in the IoT, where the intended users are equipped with buffers for cooperation. The direct transmission from the access point to the users and the buffer-aided cooperative transmission between the intended users are coordinated. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the performance of the proposed scheme, in terms of the system outage probability and average delay, is theoretically derived with closed-form expressions. Furthermore, the full diversity order of three is demonstrated to be achieved for each user pair if the buffer size is not less than three, which is larger than conventional nonbuffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system in the IoT.

This paper investigates asynchronous reinforcement learning algorithms for joint buffer-aided relay selection and power allocation in the non-orthogonal-multiple-access (NOMA) relay network. With the hybrid NOMA/OMA transmission, we investigate joint relay selection and power allocation to maximize the throughput with the delay constraint. To solve this complicated high-dimensional optimization problem, we propose two asynchronous reinforcement learning-based schemes: the asynchronous deep Q-Learning network (ADQN)-based scheme and the asynchronous advantage actor-critic (A3C)-based scheme, respectively. The A3C-based scheme achieves better performance and robustness when the action space is large, while the ADQN-based scheme converges faster with a small action space. Moreover, a-prior information is exploited to improve the convergence of the proposed schemes. The simulation results show that the proposed asynchronous learning-based schemes can learn from the environment and achieve good convergence.

Millimeter-wave (mmWave) and ultra-dense networks are two key technologies for the fifth-generation (5G) and beyond communication system. However, the ultra-dense deployment of small base stations (SBSs) might introduce severe interference to users that connect to SBSs. This paper analyzes the performance of 5G communication networks where the SBSs with coordinated beamforming, operating at mmWave frequency band and macro base stations (MBSs) operating at sub-6 GHz coexist. First, by utilizing a stochastic geometry approach, we obtain the cell association probability expressions in terms of different cell association biases, base station density ratios and probabilities of line of sight (LoS) link. Furthermore, we propose a clustering method to choose some SBSs to eliminate intra-cell interference. Then, we put forward an average distance from the Kth SBS to a user to obtain signal-to-interference-ratio (SINR) and rate coverage probability expressions. The simulation results validate the correctness of the expressions, and indicate that the optimal cardinality of coordinated SBSs increases with the density of SBSs. In addition, the relationship between the cluster size K and the average energy efficiency is obtained, which can be used to guide the coordination principle in 5G and beyond communication systems.

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.

This article proposes zero-forcing (ZF) beamforming strategies that can simultaneously deal with active and passive eavesdroppers in visible light communication (VLC) systems. First, we propose a ZF beamforming scheme that steers a transmission beam to the null space of active eavesdroppers' (AEDs) channel, while simultaneously considering the SNRs for a legitimate user (UE) and passive eavesdroppers (PEDs) residing at unknown locations. To find an eigenmode related to the optimal beamforming vector, we adopt an inverse free preconditioned Krylov subspace projection method. For unfavorable VLC secrecy environments, the proposed ZF beamformer appears to be incapable of effectively coping with the PEDs due to the strict condition that the data transmission must be in the null space of the AEDs' channel matrix. Hence, an alternative beamforming scheme is proposed by relaxing the constraint on the SNRs of the AEDs. The related optimization problem is formulated to reduce the secrecy outages caused by PEDs, while simultaneously satisfying the target constraints on the SNRs of the UE and the AEDs. To simplify the mathematical complexity of the approach, Lloyd's algorithm is employed to sample the SNR field, which in turn discretizes the problem, thus making it tractable for practical implementation. The numerical results show that both the exact and relaxed ZF beamforming methods achieve superior performance in the sense of secrecy outage relative to a benchmark ZF scheme. Moreover, the proposed relaxed ZF beamforming method is shown to cope with PEDs better than the exact ZF beamforming approach for unfavorable VLC environments.

This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes.

This paper considered an energy-harvesting based secure two-way relay (EH-STWR) network, where two users exchanged information with the assistance of one buffer-aided relay that harvested energy from two users. To realize the confidential message exchange between two users in the presence of a potential eavesdropper, a secure bidirectional relaying scheme based on time division broadcast (TDBC) was proposed, where one user sent artificial noise to suppress the eavesdropper and another user transmitted data to the relay. A secure sum-rate maximization problem was formulated subject to average and peak transmit power constraints, data buffer and energy storage causality, and transmission mode constraints. By employing the Lyapunov optimization framework, a security-aware adaptive transmission scheme was proposed to jointly adapt transmission mode selection, power allocation, and security rate allocation according to channel/buffer/energy state information (CSI/BSI/ESI). Analysis results showed that the average achievable secrecy rate region can be significantly improved and there exists an inherent trade-off among transmission delay, requirement of transmit power consumption, and achievable secure sum-rate. Moreover, the channel condition between the energy-constrained relay and the potential eavesdropper is a critical factor on the achievable long-term average secrecy rate performance.

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user's choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

This paper proposes a novel zero-forcing (ZF) beamforming strategy that can simultaneously cope with active and passive eavesdroppers (EDs) in visible light communication systems. A related optimization problem is formulated to maximize the signal-to-noise ratio (SNR) of the legitimate user (UE) while suppressing the SNR of active ED to zero and constraining the average SNR of passive EDs. The proposed beamforming directs the transmission along a particular eigenmode related to the null space of the active ED channel and the intensity of the passive ED point process. An inverse free preconditioned Krylov subspace projection method is used to find the eigenmode. The numerical results show that the proposed ZF beamforming scheme yields better performance relative to a traditional ZF beamforming scheme in the sense of increasing the SNR of the UE and reducing the secrecy outage probability.

In this paper, we investigate a Deep Reinforcement Learning based delay-constrained relay selection for secure buffer aided Cognitive Relay Networks (CRNs). We model the relay selection problem in secure butter-aided CRNs as a Markov Decision Process (MDP) problem, and introduce Deep Q-Learning to solve this MDP problem. In the proposed scheme, delay constraint is considered when the packets arriving at the receiver in CRNs. Moreover, we consider the security of data transmissions in butter-aided CRNs with an eavesdropper which can intercept the signals from the source and relays. Furthermore, we introduce ε-greedy strategy to balance the exploitation and exploration. The result shows compared with Max-Ratio scheme, the proposed scheme enhances the throughput with both delay and security constrained significantly in secure CRNs.

We analyse the performance of a communication link assisted by an intelligent reflective surface (IRS) positioned in the far field of both the source and the destination. A direct link between the transmitting and receiving devices is assumed to exist. Perfect and imperfect phase adjustments at the IRS are considered. For the perfect phase configuration, we derive an approximate expression for the outage probability in closed form. For the imperfect phase configuration, we assume that each element of the IRS has a one-bit phase shifter (0°,180°) and an expression for the outage probability is obtained in the form of an integral. Our formulation admits an exact asymptotic (high SNR) analysis, from which we obtain the diversity orders for systems with and without phase errors. We show these are N+1 and 1/2 (N+3), respectively. Numerical results confirm the theoretical analysis and verify that the reported results are more accurate than methods based on the central limit theorem (CLT).

Covert communication provides high-level security for protecting users' privacy information. In this paper, we analyze the joint impact of an external jammer and channel uncertainty on covert communication in multi-user cognitive radio networks. Meanwhile, to fairly schedule the covert communication over multi-user cognitive radio networks, we propose a fairness secondary user (SU) scheduling scheme, which enables each SU to have the same probability for sending information covertly with the aid of an external jammer. Then, the closed-form expression for the covert rate of the scheduled SU can be obtained. Our results show that the minimal detection error probability and covert rate of the scheduled SU can be significantly improved by exploiting the channel uncertainty and random variation of interference power. Moreover, the impact of interference power on the probability of detection error and the covert rate is noticeable when channel uncertainty is large.

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

Multi-hop route optimization in large-scale inhomogeneous networks is typically NP-hard, for most problem formulations, requiring the application of heuristics which, despite their relatively low processing complexity, find suboptimal solutions. Where optimal solutions can be determined by Lagrangian based constrained optimization techniques for example, the processing complexity typically scales like O(N3), N being the number of relays employed. Here, we propose an alternative approach to route optimization by considering the limit of infinite relay node density to develop a continuum model, which yields an optimized equivalent continuous relay path. The model is carefully constructed to maintain a constant connection density even though the node density scales without bound. This leads to a formulation for minimizing the end-to-end outage probability that can be solved using methods from the calculus of variations. With the continuum model, we show that the processing complexity scales linearly with the number of points that sample the continuous path, which can be lower than the number of relay nodes in a large scale network. We demonstrate the effectiveness of this new approach and its potential by considering a network subjected to point sources of interference.

This paper considers secure simultaneous wireless information and power transfer (SWIPT) in cell-free massive multiple-input multiple-output (MIMO) systems. The system consists of a large number of randomly (Poisson-distributed) located access points (APs) serving multiple information users (IUs) and an information-untrusted dual-antenna active energy harvester (EH). The active EH uses one antenna to legitimately harvest energy and the other antenna to eavesdrop information. The APs are networked by a centralized infinite backhaul which allows the APs to synchronize and cooperate via a central processing unit (CPU). Closed-form expressions for the average harvested energy (AHE) and a tight lower bound on the ergodic secrecy rate (ESR) are derived. The obtained lower bound on the ESR takes into account the IUs' knowledge attained by downlink effective precoded-channel training. Since the transmit power constraint is per AP, the ESR is nonlinear in terms of the transmit power elements of the APs and that imposes new challenges in formulating a convex power control problem for the downlink transmission. To deal with these nonlinearities, a new method of balancing the transmit power among the APs via relaxed semidefinite programming (SDP) which is proven to be rank-one globally optimal is derived. A fair comparison between the proposed cell-free and the colocated massive MIMO systems shows that the cell-free MIMO outperforms the colocated MIMO over the interval in which the AHE constraint is low and vice versa. Also, the cell-free MIMO is found to be more immune to the increase in the active eavesdropping power than the colocated MIMO.

Mobile edge caching (MEC) and device-todevice (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

In this paper, a low-complexity cell-edge-aware Antenna Selection (AS) algorithm is proposed for a Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) downlink system with Matched Filter (MF) precoding. We assume that the users are uniformly distributed in the cell, and therefore, have different Signal-to-Interference plus Noise Ratios (SINRs). At each iteration, the proposed algorithm selects one antenna to reduce the highest interference term between any two users to its minimum value. Furthermore, we utilize a Max-Min Power Allocation (MMPA) scheme to further enhance the performance of cell-edge users and achieve higher fairness. In addition, the complexity of the proposed AS algorithm is evaluated in terms of number of floating-point operations (FLOPs) required for its implementation. Finally, our proposed AS method is compared with other low-complexity AS schemes found in the literature and shown to demonstrate an impressive performance-complexity trade-off.

This paper proposes a secrecy enhancement mechanism for multiuser visible light communication (VLC) systems. Thanks to the inherent advantages of visible light that it cannot penetrate opaque walls and its channel gain largely depends on the distance, VLC systems can serve multiple users at a time with high security and dense spatial reuse. Nevertheless, in the presence of multiple users, the interference caused by other users' signals should be carefully considered when analyzing the secrecy rate and data rate performance measures. By employing a continuous LED model, we formulate an optimization problem to find the optimal set of LEDs that should be used for communication such that the average secrecy rate for the secured user is maximized while satisfying data rate requirements for the other ordinary users. Numerical results are provided to verify that the relative locations of multiple users, the spatial distribution of a random eavesdropper, and the required data rates are significant factors that affect the secrecy performance in multiuser VLC systems.

In this paper, we propose a secure visible light communication (VLC) system with a novel spatial jamming scheme, which is inspired by practical observations of indoor VLC environments. In reality, probable and approximate locations of VLC users can be anticipated by analyzing the user behavior characteristic and the layout of the room. Based on the available location knowledge of a legitimate user (UE) and an eavesdropper (ED), an LED transmitter can choose to convey data or a jamming signal. We call this strategy spatial jamming. By employing a continuous LED model, the related optimization problems are formulated and analyzed based on the signal-to-interference-plus-noise ratio and the secrecy rate, respectively. The numerical results are provided to validate the prediction that the proposed spatial jamming scheme can effectively secure a VLC transmission even when the LEDs do not know the exact location of the ED.

The nonorthogonal multiple access (NOMA) well improves the spectrum efficiency which is particularly essential in the Internet of Things (IoT) system involving massive number of connections. It has been shown that applying buffers at relays can further increase the throughput in the NOMA relay network. This is however valid only when the channel signal-to-noise ratios (SNRs) are large enough to support the NOMA transmission. While it would be straightforward for the cooperative network to switch between the NOMA and the traditional orthogonal multiple access (OMA) transmission modes based on the channel SNR-s, the best potential throughput would not be achieved. In this paper, we propose a novel prioritization-based buffer-aided relay selection scheme which is able to seamlessly combine the NOMA and OMA transmission in the relay network. The analytical expression of average throughput of the proposed scheme is successfully derived. The proposed scheme significantly improves the data throughput at both low and high SNR ranges, making it an attractive scheme for cooperative NOMA in the IoT.

Multihop relaying is a fundamental technology that will enable connectivity in large-scale networks such as those encounted in Internet of Things applications. However, the end-to-end transmission rate decreases dramatically as the number of hops increases when half-duplex (HD) relaying is employed. In this paper, we investigate the outage probability and symbol-error rate for both HD and full-duplex (FD) transmission schemes in multihop networks subject to interference from randomly distributed third-party devices. We model the locations of the interfering devices as a Poisson point process. We derive a closed-form expression for the outage probability and approximations for the symbol-error rate for HD and FD transmissions employing BPSK and QPSK. The symbol-error rate results are obtained by using a Markov chain model for the multihop decode-and-forward links. This model accurately accounts for the nonlinear dynamical nature of the network, whereby erroneous symbol decoding can be “corrected” by a second erroneous decoding operation later in the network. We verify the analytical results through simulations and show the HD and FD schemes can be utilized to reduce the error-rate and outage probability of the system according to different residual self-interference levels and interferer densities. The results provide clear guidelines for implementing HD and FD in multihop networks.

This paper considers physical layer security enhancement mechanisms that utilize simultaneous beamforming and jamming in visible light communication systems with a randomly located eavesdropper under the assumption that there are multiple light-emitting diode (LED) transmitters and one intended user. When an eavesdropper with an augmented front-end receiver is present, the jamming is very useful for preventing the eavesdropper from wiretapping the information since it is not possible to extract only the information component from the received signal if the jamming signal is random. Thus, in this paper, an optimization problem is formulated with a focus on the signal-to-interference-plus-noise ratio for the legitimate link, and it is solved by a heuristic method called the concave-convex procedure. Then, a ternary scheme is proposed, which is less complicated than the full (joint) scheme, and it is optimized by adopting a formulation based on an assignment problem, the solution of which is effectively obtained by the so-called tabu search procedure. In addition, the problem of maximizing the average secrecy rate is investigated by utilizing a continuous LED model, which significantly relaxes the complication that rises from calculating the expectation with respect to the location of the eavesdropper. Our analysis and simulation results show that the proposed simultaneous beamforming and jamming strategies (both joint and ternary) are good proxies for maximizing the average secrecy rate by utilizing the statistical information on the eavesdropper's random location.

Unmanned aerial vehicles (UAVs) have been undergoing fast development for providing broader signal coverage and more extensive surveillance capabilities in military and civilian applications. Due to the broadcast nature of the wireless signal and the openness of the space, UAV eavesdroppers (UEDs) pose a potential threat to ground communications. In this paper, we consider the communications of a legitimate ground link in the presence of friendly jamming and UEDs within a finite area of space. The spatial distribution of the UEDs obeying a uniform binomial point process (BPP) is used to characterize the randomness of the UEDs. The ground link is assumed to experience log-distance path loss and Rayleigh fading, while free space path loss with/without the averaged excess path loss due to the environment is used for the air-to-ground/air-to-air links. A piecewise function is proposed to approximate the line-of-sight (LoS) probability for the air-to-ground links, which provides a better approximation than using the existing sigmoid-based fitting. The analytical expression for the secure connection probability (SCP) of the legitimate ground link in the presence of non-colluding UEDs is derived. The analysis reveals some useful trends in the SCP as a function of the transmit signal to jamming power ratio, the locations of the UAV jammer, and the height of UAVs.

In this paper, we demonstrate a visible light communication (VLC) system using continuous phase modulation (CPM) and prove its advantage in energy efficiency over orthogonal frequency-division multiplexing (OFDM) scheme. Furthermore, to ensure the feasibility of broadband communications in indoor optical multipath environments, we propose algorithms including linear decomposition-based channel compensation to combat the inter-symbol-interference (ISI) and correlation matrix-based highly power-efficient CPM signal reconstruction to recover the original CPM signal without multipath effects. Additionally, in the signal reconstruction algorithm, a condition-enhanced method is proposed to solve the condition number problem of correlation matrix. The algorithms are verified and evaluated in terms of the normalized mean squared error (NMSE) simulation and bit error rate (BER) experiments. The power efficiency, the fading compensation character promote the proposed CPM transceiver to be a practical VLC candidate.

Considering reflected light in physical layer security (PLS) is very important because a small portion of reflected light enables an eavesdropper (ED) to acquire legitimate information. Moreover, it would be a practical strategy for an ED to be located at an outer area of the room, where the reflection light is strong, in order to escape the vigilance of a legitimate user. Therefore, in this paper, we investigate the impact of multipath reflections on PLS in visible light communication in the presence of randomly located eavesdroppers. We apply spatial point processes to characterize randomly distributed EDs. The generalized error in signal-to-noise ratio that occurs when reflections are ignored is defined as a function of the distance between the receiver and the wall. We use this error for quantifying the domain of interest that needs to be considered from the secrecy viewpoint. Furthermore, we investigate how the reflection affects the secrecy outage probability (SOP). It is shown that the effect of the reflection on the SOP can be removed by adjusting the light emitting diode configuration. Monte Carlo simulations and numerical results are given to verify our analysis.

Sparse code multiple access (SCMA) is a promising candidate air interface of the next generation mobile networks. However, the decoding complexity of current message passing algorithm for SCMA is very high. In this study, the authors map SCMA constellation to q ‐order Galois field ( ) and introduce a trellis representation to SCMA. Based on the trellis representation, they propose low‐complexity decoding algorithms for SCMA by using truncated messages, which is referred as extended max‐log (EML) algorithm. As the truncated length of EML is unitary for each user, they further propose a channel‐adaptive EMLalgorithm to truncate the messages with a rule that can be adaptive to the channel state. Simulation results show that the proposed schemes obtain a low computational complexity with only a slight performance degradation when the truncated length is selected appropriately.

With the development of wireless communications and the intellectualization of machines, the Internet of Things (IoT) has been of interest to both industry and academia. Multihop routing and relaying are key technologies that will underpin IoT mesh networks in the future. This paper investigates optimal routing based on the trusted connectivity probability (T-CP) for multihop, underlay, device-to-device (D2D) communications with decode-and-forward relaying. Both random and fixed locations for base stations (BSs) are considered, where the former case assumes that the locations of the BSs are modeled as a Poisson point process (PPP). First, we derive two expressions for the connectivity probability (CP): 1) a tight lower bound and 2) an exact closed-form. Analysis is carried out for the cases where the channel state information (CSI) between BSs and the D2D transmitter is known (CSI-aware) and unknown (noCSI). Interference from active cellular user equipments (CUEs) is characterized by modeling CUE locations as a PPP. Moreover, motivated by results that have shown that social behavior leads to D2D devices communicating with nearby neighbors, we derive the trust probability for D2D connections by using a rank-based model. Finally, we propose a novel routing algorithm that can achieve the highest T-CP for any pair of D2D devices in a distributed manner. The derived analytical results are verified by Monte Carlo simulations. We show that the proposed routing algorithm achieves almost the same performance as that attained through an exhaustive search. When BSs are located randomly, the optimal path based on the CP is the shortest path between the D2D transmitter and receiver. However, for fixed BSs, the optimal path selection depends on the locations of the BSs, which provides a very useful insight in designing the multihop D2D system for 5G IoT.

This paper considers secrecy enhancement mechanisms in visible light communication (VLC) systems with spatially distributed passive eavesdroppers (EDs) under the assumption that there are multiple LED transmitters and one legitimate user equipment. Based on certain amplitude constraints, we propose a beamforming scheme to improve secrecy performance. Contrary to the case where null-steering is made possible by using knowledge of the ED locations, the proposed beamforming when only statistical information about ED locations is available directs the transmission along a particular eigenmode related to the intensity of the ED process and the intended channel. Then, a LED selection scheme that is less complicated than beamforming is provided to reduce the secrecy outage probability (SOP). An approximate closed-form for the SOP is derived by using secrecy rate bounds. All the analysis is numerically verified by Monte-Carlo simulations. The analysis shows that the beamformer yields superior performance to LED selection. However, LED selection is still a highly efficient alternative scheme due to the complexity associated with the use of multiple transmitters in the full beamforming approach. These performance trends and exact relations between system parameters can be used to develop a secure VLC system in the presence of randomly distributed EDs.

In this paper, we propose an adaptive orthogonal frequency-division multiplexing with index modulation (OFDM-IM) for two-hop relay networks. In contrast to the traditional OFDM-IM with a deterministic and fixed mapping scheme, in this proposed adaptive OFDM-IM, the mapping schemes between a bit stream and indices of active subcarriers for the first and second hops are adaptively selected by a certain criterion. As a result, the active subcarriers for the same bit stream in the first and second hops can be varied in order to combat slow frequency-selective fading. In this way, the system reliability can be enhanced. In addition, considering the fact that a relay device is normally a simple node, which may not always be able to perform mapping scheme selection due to limited processing capability, we also propose an alternative adaptive methodology in which the mapping scheme selection is only performed at the source and the relay will simply utilize the selected mapping scheme without changing it. The analyses of average outage probability, network capacity, and symbol error rate are given in closed form for decode-and-forward relaying networks and are substantiated by numerical results generated by Monte Carlo simulations.

In this paper, we propose a novel codebook design scheme for orthogonal frequency-division multiplexing with index modulation (OFDM-IM) to improve system performance. The optimization process can be implemented efficiently by the lexicographic ordering principle. By applying the proposed codebook design, all subcarrier activation patterns with a fixed number of active subcarriers will be explored. Furthermore, as the number of active subcarriers is fixed, the computational complexity for estimation at the receiver is reduced and the zero-active subcarrier dilemma is solved without involving complex higher layer transmission protocols. It is found that the codebook design can potentially provide a tradeoff between diversity and transmission rate. We investigate the diversity mechanism and formulate three diversity-rate optimization problems for the proposed OFDM-IM system. Based on the genetic algorithm, the method of solving these formulated optimization problems is provided and verified to be effective. Then, we analyze the average block error rate and bit error rate of the OFDM-IM systems applying the codebook design. Finally, all analyses are numerically verified by the Monte Carlo simulations. In addition, a series of comparisons are provided, by which the superiority of the codebook design is confirmed.

It is well recognized that using buffers in relay networks significantly improves the transmission reliability, which is often at the price of higher packet delay. Existing buffer-aided relay networks are all based on the physical links among cooperative nodes. This may, however, lead to performance degradation in practice, because those cooperative nodes may not trust each other for cooperation even though their physical connections are strong. In this paper, we propose a novel buffer-aided relay selection scheme to align data transmission with both strong and trusted links. By maintaining the buffer lengths as close as possible to the newly introduced target buffer lengths, the proposed scheme is able to balance the outage performance and packet delay. Both the outage probability and average packet delay are analyzed for spatially random relays. Particularly, we show that the outage performance may have error floors because of the trusts. The analysis shows that using buffers in trust-aware relay networks is able to either increase the diversity order or lower the error floor of the outage probability.

In this work, we analyze the distance statistics corresponding to points in a Matern cluster (offspring points) and points that do not belong to that cluster (non-offspring points). We first derive the probability density function (PDF) of the distance between an offspring point and a non-offspring point of a Matern cluster. We then formulate the probability generating functional based on this PDF. Since many wireless networks (e.g., device-to-device (D2D) networks and cognitive radio systems) exhibit device clustering, this formalism enables us to efficiently formulate and evaluate expressions that describe the interference statistics and connection probability in clustered networks. We validate our theoretical analysis with numerical simulations, and illustrate that traditional methods of evaluating similar performance metrics (based on point process statistics instead of distance statistics) are unsuitable for use in such complex scenarios.

We consider an underlay cognitive network with secondary users that support full-duplex communication. In this context, we propose the application of antenna selection at the secondary destination node to improve the secondary user secrecy performance. Antenna selection rules for cases where exact and average knowledge of the eavesdropping channels are investigated. The secrecy outage probabilities for the secondary eavesdropping network are analyzed, and it is shown that the secrecy performance improvement due to antenna selection is due to coding gain rather than diversity gain. This is very different from classical antenna selection for data transmission, which usually leads to a higher diversity gain. Numerical simulations are included to verify the performance of the proposed scheme.

In this paper, we analyze the outage performance of two multicarrier relay selection schemes, i.e. bulk and per-subcarrier selections, for two-hop orthogonal frequency-division multiplexing (OFDM) systems. To provide a comprehensive analysis, three forwarding protocols: decode-and-forward (DF), fixed-gain (FG) amplify-and-forward (AF) and variable-gain (VG) AF relay systems are considered. We obtain closed-form approximations for the outage probability and closed-form expressions for the asymptotic outage probability in the high signal-to-noise ratio (SNR) region for all cases. Our analysis is verified by Monte Carlo simulations, and provides an analytical framework for multicarrier systems with relay selection.

We analyze the secrecy outage probability in the downlink for wireless networks with spatially (Poisson) distributed eavesdroppers (EDs) under the assumption that the base station employs transmit antenna selection (TAS) to enhance secrecy performance. We compare the cases, where the receiving user equipment (UE) operates in half-duplex (HD) mode and full-duplex (FD) mode. In the latter case, the UE simultaneously receives the intended downlink message and transmits a jamming signal to strengthen secrecy. We investigate two models of (semi)passive eavesdropping: 1) EDs act independently and 2) EDs collude to intercept the transmitted message. For both of these models, we obtain expressions for the secrecy outage probability in the downlink for the HD and FD UE operation. The expressions for the HD systems have very accurate approximate or exact forms in terms of elementary and/or special functions for all path loss exponents. Those related to the FD systems have exact integral forms for general path loss exponents, while exact closed forms are given for specific exponents. A closed-form approximation is also derived for the FD case with colluding EDs. The resulting analysis shows that the reduction in the secrecy outage probability is logarithmic in the number of antennas used for TAS and identifies conditions, under which HD operation should be used instead of FD jamming at the UE. These performance trends and exact relations between system parameters can be used to develop adaptive power allocation and duplex operation methods in practice. Examples of such techniques are alluded to herein.

This paper investigates the secrecy connectivity probability for wireless networks with transmit antenna selection in the presence of randomly located eavesdroppers. Firstly, we propose an antenna selection scheme for use at the base station with a half-duplex receiver to enhance secrecy connectivity performance. Then in order to further improve the secrecy connectivity, a full-duplex (FD) receiver, which broadcasts a jamming signal while receiving the downlink message, is considered in this work. The probabilities of secrecy connectivity are given in the closed form and integral form for half-duplex and full-duplex receivers, respectively. The derived analytical results are verified by Monte Carlo simulations. The resulting analysis shows that the application of antenna selection at the transmitting base station and full-duplex communication at the receiving terminal leads to significant improvements in secrecy connectivity.

Efficient transmission power control is indispensable for cellular networks. It not only provides a high energy efficiency, but also maintains reliable connections. With the emergence of 5G mobile technology, the presence of device-to-device (D2D) communications within the cellular network has stimulated research on radio resource sharing. In this paper, we consider an underlay D2D network operating in a Rayleigh fading channel and propose a power allocation method that assigns transmit power levels to D2D UEs (DUEs) and cellular UEs (CUEs) such that the joint connection probability of DUEs and CUEs is maximized. The approach is formulated as a optimization problem, and we prove that the problem is log concave. Hence, the optimum powers for active UEs can be found easily using modern computational methods. Both the theoretical and simulated results show that the joint connectivity probability is improved by one to two orders of magnitude by applying the optimization procedure compared to conventional LTE open loop power allocation. This dramatic improvement comes at the cost of an increase in UE average transmit power. Thus, the proposed technique is well suited to 5G public safety and disaster relief communication modes where enhanced connectivity is the top priority.

In this paper, a novel joint secure resource allocation optimization is proposed for full-duplex (FD) relay networks with cooperative jamming (CJ) in the presence of multiple source-destination (SD) pairs and an eavesdropper. We first derive the expression of the secrecy capacity for a single FD relay link with CJ. Then the joint power allocation and relay subchannel assignment (JPARA) optimization is proposed to maximize the sum secrecy capacity of the network. The proposed optimization is evaluated by numerical results, which prove that significant performance gain can be achieved by full-duplex relays when the self-interference is well suppressed. Besides, the cooperative jamming scheme is shown to improve the throughput effectively in the FD mode, while higher gap tends to be achieved by CJ in the HD mode.

An attractive hybrid method of mitigating the effects of the residual self-interference imposed by the signal propagation and analog/digital circuit non-idealities for Full-Duplex (FD) point-to-point multiple-input multiple-output (MIMO) systems is proposed. Furthermore, the effect of channel estimation errors on system performance is considered. The simulation results demonstrate that our proposed cancellation technique for FD systems achieve a significant gain over traditional Half-Duplex (HD) systems. For example, the sum rate of our proposed scheme of FD MIMO system is approximately 1.8 times higher than that of the HD transmission.

This paper investigates the secrecy performance of full-duplex relay (FDR) networks. The resulting analysis shows that FDR networks have better secrecy performance than half duplex relay networks, if the self-interference can be well suppressed. We also propose a full duplex jamming relay network, in which the relay node transmits jamming signals while receiving the data from the source. While the full duplex jamming scheme has the same data rate as the half duplex scheme, the secrecy performance can be significantly improved, making it an attractive scheme when the network secrecy is a primary concern. A mathematic model is developed to analyze secrecy outage probabilities for the half duplex, the full duplex and full duplex jamming schemes, and the simulation results are also presented to verify the analysis.

In multiple‐input multiple‐output (MIMO) systems, multiple‐antenna selection has been proposed as a practical scheme for improving the signal transmission quality as well as reducing realisation cost because of minimising the number of radio‐frequency chains. In this study, the authors investigate transmit antenna selection for MIMO systems with the Golden Code. Two antenna selection schemes are considered: max‐min and max‐sum approaches. The outage and pairwise error probability performance of the proposed approaches are analysed. Simulations are also given to verify the analysis. The results show the proposed methods provide useful schemes for antenna selection.

In this study, the authors evaluate the outage probability performance of an amplify‐and‐forward cooperative relay network where the relays are equipped with cognitive radios. When the number of available relays is more than four the authors use the channel conditions in order to select the best four cognitive relays from a set of M cognitive relay nodes and then they are used for cooperation between the source and the destination nodes. Expressions for outage probability are determined for a frequency flat Rayleigh‐fading environment from the received signal‐to‐noise ratio with perfect and imperfect spectrum acquisition. In addition, a modified distributed quasi‐orthogonal space–time block coding scheme with increased code gain distance is considered for use within the proposed cognitive relay network. To utilise the available spectrum opportunities with the modified quasi‐orthogonal space–time block code, the code matrix can be adapted to the number of available relays. Simulation results show that the four relay selection improves the system performance. This is confirmed by the outage probability analysis. The simulations also show that the modified code can significantly enhance the performance of the system and improve the reliability of the link as compared with the conventional distributed quasi‐orthogonal space–time block coding.

This paper considers the security of transmission in buffer-aided decode-and-forward cooperative wireless networks. An eavesdropper which can intercept the data transmission from both the source and relay nodes is considered to threaten the security of transmission. Finite size data buffers are assumed to be available at every relay in order to avoid having to select concurrently the best source-to-relay and relay-to-destination links. A new max-ratio relay selection policy is proposed to optimize the secrecy transmission by considering all the possible source-to-relay and relay-to-destination links and selecting the relay having the link which maximizes the signal to eavesdropper channel gain ratio. Two cases are considered in terms of knowledge of the eavesdropper channel strengths: exact and average gains, respectively. Closed-form expressions for the secrecy outage probability for both cases are obtained, which are verified by simulations. The proposed max-ratio relay selection scheme is shown to outperform one based on a max-min-ratio relay scheme.

The authors evaluate the outage probability of a cognitive amplify‐and‐forward relay network with cooperation between certain secondary users, chosen by single and multi‐relay (two and four) selection, based on the underlay approach, which requires adherence to an interference constraint on the primary user. The relay selection is performed either on the basis of a max‐min strategy or one based on maximising exactly the end‐to‐end signal‐to‐noise ratio. To realise the relay selection schemes within the secondary networks, a predetermined threshold for the power of the received signal in the primary receiver is assumed. To assess the performance advantage of adding additional secondary relays, we obtain analytical expressions for the probability density function and cumulative density function of the received SNR and thereby provide closed form and near closed form expressions for outage probability over Rayleigh frequency flat fading channels. In particular, the authors present lower and upper bound expressions for outage probability and then provide a new exact expression for outage probability. These analytical results are verified by numerical simulation.

The authors perform an outage probability analysis of a cooperative communication system which transmits over multi‐path channels with best single, or best two relay pair selection and amplify‐and‐forward two‐hop relaying. The probability density function of the multi‐path links is modelled in the time domain with an Erlang distribution function. The analytical expressions for the probability density function and cumulative density function of the end‐to‐end signal‐to‐noise ratio are obtained for an arbitrary number of relay nodes and multi‐path channel lengths of 2 and 3 with best single and best two relay pair selection from N available relays; from which outage probabilities are calculated. The spatial and temporal cooperative diversity of the network is then analysed. Finally, the theoretical results are compared with simulations to confirm the validity of the analysis, and the advantage of two relay selection is verified through bit error rate evaluation.

Cooperative diversity has been recently proposed as a way to form virtual antenna arrays and thereby mitigate the deleterious effect of fading channels in transmission. In an environment where multiple relays are available, selection of a subset of such relays may be required as, for example, in distributed space-time coding. In this study, the authors therefore use local measurements of the instantaneous channel conditions to select the best relay pair from a set of N available relays, which both come from the same cluster or different clusters, and then use these best relays for cooperation between the source and the destination. The authors also show that the best relay pair selection scheme has robustness against feedback error and outperforms a scheme based on selecting only the best single relay. The authors obtain analytical expressions for the probability density function, cumulative density function and the moment generating function of the received signal-to-noise ratio to derive closed-form expressions for outage probability over Rayleigh frequency flat-fading channels. The analytical results are supported by simulation studies.

It is the purpose of the note to point out that the Cumulative Distribution Function (CDF) (Eq. (23)) in Appendix A in the paper "Relay Selection for Secure Cooperative Networks with Jamming" by Krikidis et al. (IEEE Trans. Wireless Commun., vol. 8, no. 10, pp. 5003-5011, Oct. 2009) is not the exact expression but an approximation. We provide the exact solution of the CDF in two forms: one using Beta and hypergeometric functions and the second exploiting a recurrence relationship.