The deployment of reconfigurable intelligent surfaces (RIS) in millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is usually hindered by the dual challenges of high-dimensional channel estimation and prohibitive control phase-shift overhead. To reduce channel estimation complexity and feedback overhead, this paper constructs a unified tensor-based framework. First, by exploiting the sparsity of mmWave channels and the Vandermonde structure of antenna arrays, we formulate the received signals as a canonical polyadic (CP) model characterized by tensor decomposition uniqueness. Subsequently, a closed-form channel estimation algorithm is proposed to fit the constructed CP model and extract channel parameters. To further reduce the feedback overhead, we propose a tensor-based low-rank RIS feedback scheme. By exploiting the inherent Kronecker-structure of the optimal RIS phase-shift vector, the proposed scheme approximates it using a low-rank tensor representation, thereby enabling compression based on the estimated channel state information (CSI). Simulation results validate that the proposed framework achieves high-precision and low-complexity channel estimation while maintaining nearoptimal spectral efficiency with reduced feedback bits, particularly in line-of-sight (LoS)-dominant scenarios.

In this paper, we propose an unmanned aerial vehicle (UAV)-enabled massive multiple-input multiple-out (MIMO) non-orthogonal multiple access (NOMA) two-way relay (TWR) system, where multiple pairs of ground users (GUs) aim to exchange their information via an amplify-and-forward (AF) UAV relay. We consider that deploy a hovering rotary-wing UAV equipped with multiple antennas and operating in full-duplex (FD) mode, to provide reliable links to the GUs with emergency communications needs during periods of temporary damage to ground base stations. Minimum mean-square error (MMSE) channel estimation is employed by the UAV to obtain the air-ground channel state information (CSI), while maximum ratio combining/maximum ratio transmission (MRC/MRT) is adopted to process the signals of GUs. Analytical general closed-form expressions are derived for the sum spectrum efficiency (SE) and total energy efficiency (EE) of the proposed system in the case of imperfect CSI, as well as their respective asymptotic expressions. Based on the obtained expressions, the power scaling laws are presented. The numerical results suggest that the proposed system can achieve higher SE performance than terrestrial massive MIMO relay system due to strong line-of-sight (LoS) links, and the advantage is more obvious with more number of antennas. Furthermore, the optimal hovering height of the UAV is explored, which increases as the number of antennas increases. Also, to achieve a trade-off between SE and EE, it is desirable to increase the transmit power of the GUs when increasing the number of antennas at the UAV. Finally, we confirm the proposed scheme can better enhance the SE of multiple GU pairs communication system by comparing NOMA with orthogonal multiple access (OMA), FD and half duplex (HD) schemes.

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.

Next-generation wireless networks aim to deliver transformative improvements in data rates, latency, and reliability. Among critical enabling technologies, rate-splitting multiple access (RSMA) combined with intelligent reflecting surfaces (IRS) mounted on unmanned aerial vehicles (UAV) has gained considerable attention. In this paper, we address the secrecy energy efficiency (SEE) maximization problem for short-packet communications in UAV-mounted IRS-assisted RSMA networks operating under finite block length (FBL) conditions. Unlike traditional scenarios optimized for long packets, our approach explicitly incorporates the significant impact of decoding errors and strict latency constraints inherent to short-packet transmission. We propose a comprehensive framework that jointly optimizes UAV deployment, IRS phase shifts, transmit precoding vectors, and common rate allocation. The formulated non-convex optimization problem is effectively solved via iterative decomposition and efficient approximation methods. Numerical evaluations demonstrate notable improvements in SEE compared to existing benchmark methods, underscoring the efficacy of UAV-mounted IRS and RSMA integration in addressing practical constraints of short-packet communication.

This study addresses the challenge of achieving high-precision indoor positioning in non-line-of-sight (NLoS) environments through the development of an innovative visible light positioning (VLP) system that utilizes optical intelligent reflecting surfaces (OIRS). Unlike current hybrid methodologies that combine both line-of-sight (LoS) and NLoS techniques tailored for Internet of Things (IoT) environments, our novel single-LED architecture relies solely on signals reflected by an OIRS to facilitate accurate positioning in intricate indoor settings where direct light paths are often obstructed. This system employs a two-stage maximum likelihood estimation framework that effectively integrates received signal strength (RSS) and time-of-arrival (ToA) characteristics, thereby addressing the shortcomings of traditional single-feature methods and ensuring reliable performance in densely populated IoT scenarios. To tackle the non-convex optimization problem, we propose an improved grey wolf optimization (IGWO) algorithm, which exhibits superior positioning accuracy and convergence properties when compared to particle swarm optimization and genetic algorithms. Simulation results substantiate the framework’s efficacy, demonstrating improved positioning accuracy. The proposed system presents a cost-effective solution for complex indoor environments where direct light paths are frequently obstructed, thereby advancing the practical application of VLP technologies.

Conventional visible light positioning (VLP) systems are limited by inadequate positioning accuracy and vulnerability to obstacle occlusion, thereby hindering their deployment in precision-critical applications. To address these challenges, this paper proposes a fusion algorithm that synergistically combines received signal strength (RSS) and angle of arrival (AoA) information. Furthermore, the proposed approach incorporates an intelligent reflecting surface (IRS) framework into the system model, thereby improving system robustness and simultaneously enhancing positioning accuracy under sparse light-emitting diode (LED) deployment, blockage, or non-line-of-sight (NLoS) conditions. Specifically, this paper employs a multi-photodetector (PD) array at the receiver to formulate a system of linear equations based on RSS measurements, which facilitates accurate angle estimation. This derived AoA information is subsequently fused with the RSS data to establish a joint positioning objective function, thereby mitigating the limitations associated with single-parameter approaches. Crucially, an optical IRS is integrated to produce robust NLoS propagation paths, significantly enhancing accuracy in scenarios characterized by a scarcity of LEDs or obstructed line-of-sight (LoS) links, which are common challenges in practical deployments. To address the resulting non-convex optimization problem, a dimension learning-based hunting enhanced grey wolf optimizer (GWO-DLH) is developed, ensuring efficient convergence to the global optimum. Comprehensive simulations conducted under realistic channel models demonstrate that the proposed algorithm achieves a lower root-mean-square error compared to conventional RSS-only or AoA-only methods, while maintaining a computational complexity that is comparable to state-of-the-art techniques. These findings substantiate the algorithm’s effectiveness in balancing accuracy and robustness, thereby providing a foundational framework for the advancement of high-precision indoor optical positioning systems.

To solve the problems of low system security and poor transmission quality in integrated satellite-terrestrial communication systems due to eavesdroppers and channel uncertainties, a robust resource allocation (RA) problem is studied. First, considering the constraints of users’ quality of service, the maximum transmit power of the satellite and the base station (BS), and the bounded channel uncertainties, an RA optimization problem is established by jointly optimizing the beamforming vectors, artificial noise (AN) vectors, and power allocation factors. Then, S-Procedure, successive convex approximation (SCA), and alternating optimization are adopted to convert the nonconvex problem with parameter perturbation into a convex one that can be solved efficiently. Finally, a robust RA algorithm based on the alternating approach is proposed to obtain the solutions. Simulation results show that the proposed algorithm has good security and robustness, and the outage probability is reduced by 9.12% compared to the traditional nonrobust algorithms without AN.

With the development of the sixth-generation (6G) communication networks, the scale of internet of things (IoT) devices is rapidly expanding. However, the limited computational and energy resources have become the major bottlenecks constraining IoT devices in processing complex tasks and providing high-quality services. In order to solve this problem, a novel unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surfaces (RIS) assisted mobile edge computing (MEC) with backscatter system is proposed. In this paper, a system energy efficient (EE) maximization problem is formulated by jointly optimizing the reflection coefficients, computational resources, time allocation, RIS phase shifts, and UAV trajectories while satisfying the task constraints, energy causality constraints, and trajectory constraints. To solve the established non-convex optimization problem, a three-stage alternating optimization algorithm is developed based on the Dinkelbach algorithm. The efficient solution of each subproblem is realized by leveraging the Lagrangian dual method, combined with the sub-gradient descent and successive convex approximation techniques. Furthermore, the closed-form expressions for the CPU computation frequency, backscatter reflection coefficient, and RIS phase-shift coefficients are derived. Simulation results show that the proposed scheme achieves superior system EE compared with the benchmark and simplified schemes.

This paper investigates the reliability and security of a hybrid double-reconfigurable intelligent surface (HDRIS) aided wireless-powered communication (WPC) system in the presence of eavesdroppers, where one active/passive RIS (RIS-1) is deployed between the power station and the information user, and the other passive/active RIS (RIS-2) is deployed between the information user and the access point. We propose two modes of HDRIS aided WPC, i.e., HDRIS-I with passive RIS-1 and active RIS-2, and HDRIS-II with active RIS-1 and passive RIS-2. Based on the two modes, we analyze the outage probability (OP) from the perspective of reliability and intercept probability (IP) from the perspective of security, and derive their accurate and asymptotic expressions, respectively. Moreover, a joint metric is proposed, i.e., reliability and security probability (RSP), to reveal the superiority of HDRIS compared to pure double-RIS (PDRIS). The results show that compared to PDRIS, the proposed HDRIS-I and HDRIS-II have better OPs than PDRIS-I with two passive RISs, but worse OPs than PDRIS-II with two active RISs. Both HDRIS-I and HDRIS-II have worse IPs than PDRIS-I, but better IPs than PDRIS-II. Interestingly, in HDRIS-I and HDRIS-II, the diversity gain for legitimate users is proportional to the number of elements of the RISs, while the diversity gain for eavesdroppers is only 1, indicating that HDRIS provide greater benefits for legitimate communications. In Particular, HDRIS-I achieves the best RSP under high transmission power, while HDRIS-II achieves the best RSP under low transmission power, demonstrating the superiority of HDRIS.

Energy efficiency is of paramount importance for covert integrated sensing and communication (ISAC) networks to ensure sustained operation. In light of the imperfect channel state information (CSI) encountered in practical scenarios, we investigate the energy efficiency of these networks. Taking into account a variety of CSI estimation errors, our algorithm optimizes both sensing and information beamforming design while ensuring a low detection probability by multiple untrusted wardens. The energy-efficient beamforming design is formulated as a non-convex fractional programming problem. First, we establish that the covariance matrices of communication beamforming vectors are rank-one. Subsequently, we exploit this property to transform the original problem into a semi-definite relaxed version. For Gaussian CSI estimation errors, we adopt Bernstein-type inequalities to handle the probability constraints of interception and exploit Dinkelbach’s algorithm to address the nonlinear fractional objective function. For bounded CSI estimation errors, we employ an S-procedure to tackle the non-convex constraints associated with covert communications, followed by a successive convex optimization algorithm to provide an effective solution to the original problem. Extensive simulations confirm the superiority of our proposed algorithms, demonstrating a remarkable performance gain compared with baseline schemes adopting existing approaches. Specifically, deploying a larger number of antenna elements can enhance the energy efficiency of covert ISAC networks, while simultaneously reducing the system’s total power consumption. Furthermore, the sensing beam power threshold and the outage probability of covertness serve as important trade-off parameters in covert ISAC networks.

A model-driven deep learning framework is proposed for channel estimation in Rydberg atomic quantum receivers (RAQRs) based on the measurement of holographic snapshots. Specifically, we develop a Transformer-based unrolling architecture, termed URformer, to solve the non-linear biased phase retrieval problem, which is derived by unrolling a stabilized variant of the expectation-maximization Gerchberg-Saxton (EM-GS) algorithm. Each layer of the proposed URformer incorporates three trainable modules: 1) a learnable filter network that replaces the fixed Bessel kernel in the classic EM-GS algorithm; 2) a trainable gating mechanism that adaptively combines classic updates to ensure training stability; and 3) an efficient channel Transformer module that learns to correct residual errors by capturing non-local channel dependencies. Numerical results demonstrate that the proposed URformer significantly outperforms classic iterative algorithms and conventional black-box neural networks with less pilot overhead.

This study investigates the synergy between ratesplitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to realize ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while enhancing both spectral and energy efficiency. Specifically, in the STAR-RIS-assisted multi-user RSMA network under consideration, we develop an intelligent optimization strategy that jointly designs the active beamforming at the transmitter, the allocated transmission rate for the common stream, and the passive beamforming vectors for both transmission and reflection regions of the STAR-RIS, while accounting for transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured by incorporating a bounded channel estimation error model that rigorously reflects CSI imperfections and ensures resilience against worst-case estimation errors. To tackle the highly non-convex problem, we propose an intelligent optimization algorithm that decouples the original problem into two sub-problems, which are then solved iteratively. Firstly, the active beamforming vectors for both the common and private signals are obtained by reformulating the original non-convex problem into a tractable convex semi-definite programming (SDP) framework, leveraging successive convex approximation (SCA) and semi-definite relaxation (SDR) for enhanced computational efficiency. Secondly, the passive beamforming vectors for the transmission and reflection regions of the STAR-RIS are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Additionally, when the resulting active or passive beamforming solutions are of higher rank, Gaussian randomization is employed to construct rank-one solutions. Finally, the effectiveness of the proposed optimization strategy is demonstrated through numerical simulations, which reveal significant performance gains over benchmark schemes and confirm rapid convergence.

In this paper, we investigate a Multi-layer Aerial system for GenAI inference at the Edge (MAGE). Therein, ground user equipments (UEs) request image synthesis services from a remote base station (BS) that leverages the Latent Diffusion Model (LDM) for image generation. Multiple Unmanned Aerial Vehicles (UAVs) are deployed to serve the UEs for relaying their images and prompts to the BS. To reduce the communication cost and the computation burden at the BS, the UAVs can partially execute the LDM inference, i.e., an image autoencoder and prompt encoder, and offload the diffusion process task to the BS. In this work, we aim to minimize the BRISQUE scores across all the UEs by jointly optimizing the UAVs' positions, UE-UAV associations, the number of denoising steps at the BS, and offloading strategies of the UAVs. The optimization problem is non-convex, in which the objective function based on BRISQUE scores has no closed-form expression. Due to the fixed exploration strategy of Proximal Policy Optimization (PPO), which limits the policy's adaptability in dynamic environments, this leads to sub-optimal solutions. To address these potential drawbacks, we propose an adaptive exploration strategy that dynamically adjusts the exploration rate based on observed improvements in rewards. Specifically, the exploration capability is controlled by modulating the influence of the entropy bonus according to recent reward gains. Simulations based on the COCO-Stuff datasets show that the proposed scheme outperforms baseline schemes in different terms of BRISQUE score, UAVs' energy consumption, and inference latency. In particular, the proposed scheme reduces the BRISQUE score by up to 20-28.57%, inference energy consumption up to 15.98-30.17%, transmission energy consumption by 15.4-18.5%, and the latency by up to 33.33-43.28% compared to the baseline methods, resulting in higher image quality with a noticeably improved level of perceptual naturalness, improved energy efficiency, as well as substantially faster performance.

This paper proposes a double phase shifter–based hybrid beamforming (DPS-HBF) framework for millimeter-wave NOMA systems, enabling the simultaneous realization of beam steering and beam focusing within a unified analog architecture. By superposing two independent phase-shifter vectors per beam, DPS-HBF flexibly supports heterogeneous near-field and far-field users without requiring full channel state information. To exploit this capability, a hierarchical scheduling framework combining Bitmask Dynamic Programming, Maximum Weight Matching, andk-best Semi-Greedy User Scheduling is developed to balance optimality, scalability, and computational complexity. The proposed design relies solely on low-overhead SINR feedback, making it suitable for practical large-scale deployments. Simulation results show that DPS-HBF consistently outperforms existing hybrid beamforming and orthogonal multiple access baselines in terms of sum-rate and fairness, achieving up to 30–35% throughput gains over the strongest benchmark under moderate-to-high SNR conditions.

Integrated sensing and communication (ISAC) has gained traction in academia and industry. Recently, multipath components (MPCs), as a type of spatial resource, have the potential to improve the sensing performance in ISAC systems, especially in richly scattering environments. In this paper, we propose to leverage MPC and Khatri-Rao space-time (KRST) code within a single ISAC system to realize high-accuracy sensing for multiple dynamic targets and multi-user communication. Specifically, we propose a novel MPC-enhanced sensing processing scheme with symbol-level fusion, referred to as the “SL-MSP” scheme, to achieve high-accuracy localization of multiple dynamic targets and empower the single ISAC system with a new capability of absolute velocity estimation for multiple targets with a single sensing attempt. Furthermore, the KRST code is applied to flexibly balance communication and sensing performance in richly scattering environments. To evaluate the contribution of MPCs, the closed-form Cramér-Rao lower bounds (CRLBs) for location and absolute velocity estimation are derived. Simulation results illustrate that the proposed SL-MSP scheme is more robust and accurate in localization and absolute velocity estimation compared with the existing state-of-the-art schemes.

This paper investigates the secure communication capabilities of a non-orthogonal multiple access (NOMA) network supported by a STAR-RIS (simultaneously transmitting and reflecting reconfigurable intelligent surface) deployed on an unmanned aerial vehicle (UAV), in the presence of passive eavesdroppers. The STAR-RIS facilitates concurrent signal reflection and transmission, allowing multiple legitimate users-grouped via NOMA-to be served efficiently, thereby improving spectral utilization. Each user contends with an associated eavesdropper, creating a stringent security scenario. Under Nakagami fading conditions and accounting for phase shift inaccuracies in the STAR-RIS, closed-form expressions for the ergodic secrecy rates of users in both transmission and reflection paths are derived. An optimization framework is then developed to jointly adjust the UAV's positioning and the STAR-RIS power splitting coefficient, aiming to maximize the system's secrecy rate. The proposed approach enhances secure transmission in STAR-RIS-NOMA configurations under realistic hardware constraints and offers valuable guidance for the design of future 6G wireless networks.

This paper investigates the joint channel estimation and localization problem for integrated sensing and communication (ISAC) systems assisted by a reconfigurable intelligent surface (RIS). The key technical challenges involve addressing the computational complexity of high-dimensional sparse recovery while effectively exploiting geometric relationships in parameter estimation. To begin with, we formulate the joint estimation problem as a geometrically-constrained sparse recovery task based on maximum a posteriori estimation principles, incorporating physical feasibility constraints. Then, we propose the geometrically-constrained separable multidimensional orthogonal matching pursuit (GSMOMP) algorithm, which iteratively executes three key procedures: (1) coarse candidate selection via separable projections to avoid high-dimensional tensor construction; (2) geometric feasibility filtering to eliminate physically implausible propagation paths; and (3) refined atom selection with joint parameter updates that concurrently optimize both channel estimates and location parameters. The convergence is ensured by analytically demonstrating the monotonic decrease of the residual error. Finally, numerical results verify the effectiveness of the proposed algorithm, which achieves substantial computational efficiency improvement compared to conventional MOMP while maintaining comparable localization and angular estimation accuracy.

This paper proposes a robust received signal strength (RSS)-based localization framework for non-cooperative unmanned aerial vehicles. Conventional RSS methods face three fundamental obstacles: susceptibility to heavy-tailed measurement noise, intractable non-convexity, and severe accuracy degradation when target transmission power is unknown. These vulnerabilities present critical security risks to emerging low-altitude economy networks. To overcome these limitations, we propose an integrated joint-estimation architecture. First, a cascaded preprocessing pipeline, combining Gaussian outlier suppression and statistical median weighting, is developed to mitigate multipath-induced biases and minimize variance. Second, an information-theoretic base station (BS) selection mechanism is designed to identify geometrically optimal BSs, thereby exponentially reducing computational overhead in both uniform and random deployment scenarios. Third, the power-unknown problem is reformulated via semidefinite programming, absorbing the unknown parameter into a higher-dimensional convex cone to guarantee global convergence without relying on initial guesses. Extensive Monte Carlo simulations demonstrate that under uniform BS deployment, our strategy achieves sub-10-meter accuracy (approximately 5 m root mean square error) using only 5 selected BSs in typical urban conditions with a path loss exponent of 3. Consequently, this approach delivers a highly accurate and computationally efficient solution for real-time target tracking in complex environments.

This paper explores the security issues of a rate-splitting multiple access (RSMA)-aided integrated sensing and communication (ISAC) system. A dual-function base station communicates with multiple users via rate-splitting technology and simultaneously senses multiple targets, which also act as colluding malicious eavesdroppers. For the case where coarse target locations are known but subject to estimation errors, we construct an equivalent wiretap channel model incorporating channel uncertainty under the collusion scenario. Based on this model, the transmit covariance matrix is optimized for the sensing-only task, representing an ideal sensing-oriented transmitter design. However, in ISAC systems, the practical transmit covariance also needs to account for communication performance. In order to improve the sensing performance while ensuring communication security, we formulate an optimization problem that jointly optimizes transmit beamforming and rate-splitting to minimize covariance mismatch, subject to secrecy rate and transmit power constraints. This non-convex problem is transformed using convex relaxation techniques and solved via a robust block coordinate descent algorithm. We further consider the case where the locations of malicious sensing targets are unknown, and then formulate an optimization problem to minimize communication power while enhancing sensing capability via an omnidirectional beam, subject to rate constraints. We then develop an efficient block-wise algorithm based on convex reformulation. Simulation results confirm the efficacy of the proposed RSMA scheme in addressing system uncertainties, as well as reveal influences of various key parameters.

FAS-enabled cooperative non-orthogonal multiple access (FAS-CoNOMA) systems capture the potential of fluid antenna systems in enhancing network performance. In this system, a base station (BS) transmits a superposition signal to a central user (CU) and a cell-edge user (EU), both equipped with FAS. Specifically, the CU decodes the signal intended for the EU and cooperatively relays it to improve the EU’s communication performance. The EU employs selective combining (SC) or maximum ratio combining (MRC) to receive signals from both the BS and CU. By leveraging the dynamic properties of FAS to improve user differentiation, the CoNOMA system effectively enhances network performance compared to traditional NOMA, OMA, and fixed position antenna (FPA) systems. To address the challenging spatial correlation properties in FAS, we utilize the block-diagonal matrix approximation (BDMA) model to calculate the outage probabilities for both the CU and EU. We then derive upper bound, lower bound, and asymptotic approximation of the outage probabilities to gain deeper insights. Furthermore, we optimize the EU’s outage probability under the CU’s outage constraint and total transmit power limits by adjusting the power allocation coefficient for the CU and the transmit powers for both the BS and CU. To simplify the optimization process, we reduce the number of variables and apply the alternating optimization (AO) algorithm to break down the problem into two sub-problems. Each sub-problem is solved using the bisection search method and gradient descent algorithm (GDA). Simulation results demonstrate that FAS significantly improves outage performance, especially for the EU, and that CoNOMA notably captures the potential of FAS beyond NOMA and OMA, offering a promising solution for future wireless networks.

Wireless medical sensor networks (WMSNs) have been widely adopted in healthcare for collecting users' physiological data, providing crucial references for medical diagnosis and prevention. However, transmitting sensitive data over public networks faces security risks, potentially leading to privacy breaches and financial losses. Moreover, large-scale data transmission increases energy consumption, hindering continuous monitoring. Therefore, achieving energy efficiency alongside data security is critical for WMSNs. This paper proposes a lightweight slope-based piecewise linear approximation algorithm for online data compression, utilizing slope intervals under a user defined error bound, to reduce energy consumption. Concurrently, we introduce a pairing-free certificateless aggregate signature scheme, proven secure under the random oracle model against different type adversaries, to enhance data privacy and integrity. Experimental results demonstrate that the compression algorithm achieves efficient compression while preserving trends, and the aggregate signature scheme reduces computational overhead by 20% without increasing communication costs.

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

With the rapid growth of high-speed rail (HSR) networks, reliable communication is increasingly challenging due to complex terrain and coverage gaps in remote areas. Uncrewed aerial vehicles (UAVs), with their mobility and flexibility, provide aerial relay support to bridge these gaps, enhance signal strength, and improve HSR communication reliability. However, their performance in millimeter-wave (mmWave) systems is significantly degraded by mechanical jitter caused by environmental factors such as wind and turbulence, which adversely affects beam alignment and overall communication quality. To address these challenges, this work introduces a comprehensive analytical framework. We first develop a statistical model that characterizes the relationship between beam gain and jitter intensity. Subsequently, closed-form expressions are derived for the outage probability and ergodic data rate of UAV-assisted HSR mmWave systems under jitter influence, taking into account both co-located (CA) and distributed antenna (DA) configurations. Furthermore, we propose an adaptive beamwidth design that maximizes the average ergodic rate by adjusting the beamwidth according to UAV jitter severity. Numerical simulations verify that this approach significantly improves system capacity and robustness compared with conventional static beamforming, confirming its effectiveness.

The rapid proliferation of the Internet of Things (IoT) has made resource exchange and collaboration across diverse IoT domains commonplace, necessitating secure and privacy-preserving cross-domain authentication. However, existing schemes suffer from critical limitations: they lack time-bound access control, leading to persistent unauthorized access and heightened security risks, and most are incomplete, requiring resource-intensive redeployment of cryptographic mechanisms and increasing management overhead. To address these challenges, we propose a dynamic time-bound anonymous complete cross-domain authentication scheme that leverages consortium blockchain for decentralized trust, embeds dual temporal constraints, expiration time and permissible authentication periods, into credentials for fine-grained access control and automatic natural revocation, and employs accumulators and non-interactive zero-knowledge proofs (NIZKs) to enable anonymous authentication while ensuring strong privacy protection. Crucially, the proposed scheme achieves complete cross-domain authentication without modifying existing cryptographic mechanisms, significantly reducing overhead in computational, communication, and storage. Security and performance analyses confirm that the proposed scheme not only guarantees robust security and privacy but also outperforms existing schemes in efficiency.

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of the BD-RIS, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model and ensure that the resulting system satisfies all energy harvesting constraints. In the considered system, we design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. To tackle the inherent non-convexity of the problem, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized in an iterative manner. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming problem through successive-convex approximation (SCA), whereas the inherently convex power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the conjugate-gradient method. Finally, numerical simulations are conducted to evaluate the performance of the proposed scheme, revealing significant performance improvements over existing benchmarks and demonstrating rapid convergence within a reasonable number of iterations.

In this paper, we propose a new concept of self-sustainable active metasurface (SAM), which exploits the dual advantages of energy harvesting in terms of self-sustainability and active metasurface in terms of information transmission, to achieve continuous operation and flexible deployment for reconfigurable intelligent surface (RIS) and simultaneously mitigate its multiplicative fading. SAM can enhance incident signals via power amplifiers and achieve self-sustainability by harvesting ambient energy. We propose three operation schemes to implement energy harvesting and information transmission for SAM, namely, time-switching based SAM (TS-SAM), power-splitting based SAM (PS-SAM), and element-splitting based SAM (ES-SAM). Then, we propose three new metrics, namely, energy-information outage probability (EIOP), energy-information intercept probability (EIIP), and secure energy efficiency ratio (SEER). The accurate and asymptotic EIOP and EIIP as well as accurate SEER for the three proposed schemes are analyzed, respectively. The results show that compared to self-sustainable passive RIS, TS-SAM and ES-SAM have better EIOPs, and PS-SAM has a better EIIP. Among the three schemes, PS-SAM achieves the best EIOP at low RF energy, while TS-SAM and ES-SAM perform better in high-energy scenarios. For EIIP, PS-SAM outperforms the other two schemes. In particular, compared to self-sustainable passive RIS, TS-SAM and ES-SAM have better SEERs, verifying the superiority of the proposed TS-SAM and ES-SAM. Among all inter-node distances, the distance between user and SAM dominates the performance. When the harvested energy and the number of reflecting elements are sufficiently large, the EIOP and EIIP of TS-SAM and ES-SAM are unrelated to the amplification factor of SAM.

In this paper, we develop a tensor-based joint multi-dimensional (polarization, angle, and time delay) channel parameter estimation algorithm for single-input multiple-output (SIMO) communication systems equipped with an electromagnetic vector sensor (EMVS) linear array. By considering the EMVS array structure and multi-path propagation environment, the received signals at the base station (BS) are constructed into a third-order parallel factor (PARAFAC) tensor model. By decomposing the constructed tensor, we design a joint structured tensor decomposition algorithm (STDA) and bilinear alternating least squares (BALS) fitting algorithm using the Vandermonde structure of the array to estimate the factor matrices containing angles, polarization, and time delay. Based on the estimated factor matrices, we employ a closed-form algorithm to extract the two-dimensional direction of arrival (2D-DoA), polarization parameters, and time delay. In addition, to provide a quantitative assessment of the proposed algorithm’s performance, we calculate the Cramér-Rao bound (CRB) as a benchmark for comparison. Simulation results indicate that the proposed algorithm achieves superior estimation accuracy and is closer to the CRB compared with the existing tri-polarized algorithms.

In this paper, we investigate a millimeter-wave secure integrated sensing and communication system assisted by the sub-connected (SC) active reconfigurable intelligent surface (SC-ARIS), where the dual-function radar and communication station (DFBS) employs a hybrid precoding structure. We formulate an optimization problem to jointly design the DFBS hybrid precoding and SC-ARIS beamforming, aiming to maximize the secure energy efficiency while ensuring communication and sensing qualities. To address the above non-convex problem, we utilize alternating optimization technique to decouple it into two subproblems, where DFBS hybrid precoding and SC-ARIS beamforming are respectively optimized. For the first one, we first propose an iterative algorithm to solve the equivalent digital precoding based on constrained concave-convex procedure, Taylor expansion, semidefinite relaxation (SDR) and fractional programming techniques. Then, the hybrid precoding is obtained rely on the manifold optimization alternating minimization technique. For the later one, we propose an iterative algorithm based on the SDR. Considering a more realistic scenario, we extend to the imperfect eavesdropping channel, and propose a robust beamforming design scheme. Finally, simulation results show the effectiveness of the proposed schemes.

Intelligent reflecting surface (IRS) has been considered a promising technology to be applied to mobile edge computing (MEC) systems, especially when offloading links are blocked or weak. However, most existing works are restricted to narrow-band channel and ideal IRS reflection model, which is not practical and may lead to significant performance degradation. Thus, we consider an IRS-enhanced wideband MEC system with practical IRS reflection model. Our objective is to minimize the weighted latency of all devices by jointly optimizing the offloading data volume, edge computing resources, BS receiving vector, and IRS basic phase shift (BPS). Since the formulated problem is non-convex, we employ the block coordinate descent (BCD) technique to decouple it into two subproblems to alternatively optimize computing and communication resources. In particular, the computing resource optimization subproblem is solved based on Karush-Kuhn-Tucker (KKT) conditions and bisection search method. While the communication resource optimization subproblem is first transformed into a weighted sum-rate maximization problem based on LDR technique and KKT conditions. Then leveraging the equivalence between sum-rate maximization and MSE minimization, it is converted into a multi-variable problem that can be effectively solved using BCD technique. Simulation results show that the proposed schemes can reduce latency by 16% compared to baseline schemes when the number of IRS elements is 100, confirming the effectiveness of considering practical IRS reflection model for wideband MEC systems.

Faster-than-Nyquist (FTN) technique promises higher capacity and spectral efficiency for wireless communications. However, existing FTN studies over doubly-selective fading (DSF) channels separate channel-induced inter-symbol interference (channel-ISI) and FTN-induced ISI (FTN-ISI) to simplify cancellation. In practical DSF scenarios, the inherent coupling between FTN-ISI and channel-ISI causes significant performance degradation in conventional detection algorithms. To address this limitation, we first derive a practical FTN transmission model over DSF channels and construct the corresponding coupled interference matrix. Considering that data detection relies on efficient channel estimation, we propose a channel estimation algorithm with joint recovery of resolvable subpath parameters. This algorithm decomposes propagation paths into resolvable subpaths with independent delay-Doppler characteristics, achieving enhanced estimation accuracy through joint gain-phase optimization. Finally, building on the derived transceiver model and coupled interference matrix, we propose a whitening-enhanced orthogonal approximate message passing (WE-OAMP) algorithm that suppresses coupled interference through iterative linear-nonlinear estimation while maintaining spectral compactness. This algorithm constructs a whitening matrix using the FTN-ISI matrix to suppress noise correlation, and then performs detection through iterative linear and nonlinear estimation. Simulation results validate that the WE-OAMP algorithm outperforms benchmark algorithms in terms of bit error rate performance, especially in coded systems. Furthermore, we derive the achievable capacity of FTN signaling with WE-OAMP detection, demonstrating capacity improvement compared to Nyquist systems.

In this paper, we propose a tensor-based framework for multi-user reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC), designed to enable efficient data transmission and accurate localization across far- and near-field communications. The proposed scheme introduces a two-phase nested tensor-based ISAC transmission protocol, comprising the precoding phase and the joint symbol detection and target localization (JSDTL) phase. During the precoding phase, a third-order tensor is constructed to extract angle information from the far-field base station (BS)-RIS channel links, which is then utilized to design a precoding strategy that mitigates inter-user interference. In the JSDTL phase, the received signals are constructed into a fourth-order nested tensor incorporating angular, temporal, and coding dimensions. The algebraic structure of the nested tensor, combined with the second-order Fresnel approximation for the near-field channel model between the RIS and user equipment (UE), is leveraged to perform data recovery and target localization. Simulation results confirm that the proposed scheme achieves superior ISAC performance with reduced computational complexity, outperforming benchmark algorithms.

In this paper, we investigate a wireless powered communication network (WPCN) in which a multi-antenna hybrid access point (HAP) communicates with multiple Internet-of-Things (IoT) devices, assisted by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). In the energy transfer (ET) phase, the IoT devices harvest energy from the HAP with a nonlinear energy harvesting (EH) model, and subsequently transmit information signals to the HAP during the information transmission (IT) phase. To explore its full potential, the aSTAR-RIS employs energy splitting (ES), mode switching (MS), and time switching (TS) protocols. A sum rate maximization problem is formulated for each protocol, which jointly optimize the beamforming at the HAP, allocation of time slots and transmitting power for the IoT devices, and the adaptation of the aSTAR-RIS coefficients. To address the optimization problem with multiple coupled variables and complex non-convex constraints, we firstly decompose it into several subproblems. Specifically, to optimize the coefficients of the aSTAR-RIS in the IT phase, we develop a fractional programming-based successive convex approximation algorithm to handle the fractional objective function and the minimum rate constraints. Moreover, to obtain the coefficients of the aSTAR-RIS during the ET phase, we design a penalty-based SCA algorithm to address the binary constraints in the MS protocol and the rank-one constraints. Numerical results demonstrate that 1) employing the aSTAR-RIS in WPCNs can realize the extraordinary sum rate gain in comparison with the benchmarks of the active RIS and the passive STAR-RIS; 2) among the three operation protocols, the ES demonstrates the best performance, with the MS following closely behind, while the TS is the least effective; 3) as the minimum required data rate for each IoT device decreases, the performance gap among the three protocols becomes narrower.

Integrated sensing and communication (ISAC) is a key technology for improving spectrum efficiency and enabling intelligent wireless networks, yet its deployment in short-packet transmission scenarios faces significant challenges such as finite block-length (FBL) effects, channel estimation uncertainty, and limited coverage. To address these issues, this paper investigates a short-packet ISAC system assisted by a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and proposes a two-stage ISAC transmission paradigm. In Stage I, the hybrid STAR-RIS performs target direction-of-arrival estimation, and a closed-form expression of Cramér–Rao Bound (CRB) is derived to establish channel state information (CSI) uncertainty model based on CRB. Meanwhile, each user performs channel estimation locally and feeds results back to DFBS. In Stage II, the estimated CSI is utilized to jointly design resource allocation, and an optimization problem is formulated to maximize target illumination power under FBL and CSI uncertainty constraints. To tackle this strongly coupled non-convex problem, we develop a hierarchical solution strategy: the sensing duration is first determined via one-dimensional search, and then, an alternating optimization framework is employed to decouple the problem into DFBS beamforming and hybrid STAR-RIS coefficient optimization, where iterative algorithms based on semi-definite relaxation, semi-definite programming, and singular value decomposition are proposed to ultimately obtain a convergent optimal solution. Simulation results validate the fast convergence and superior performance of our proposed algorithm, reveal the inherent trade-off between the two stages under constrained resources, and demonstrate the importance of joint two-stage resource design assisted by hybrid STAR-RIS in enhancing short-packet ISAC system performance.

In this paper, we design a novel two-stage transmission framework in millimeter wave-ISAC systems with multiple communication users (CUs) and multiple target scenarios. In stage I, the dual-functional base station (DFBS) performs beam scanning with pilot signals, estimating target direction of arrival angles (DoAs) through the maximum likelihood estimation and multiple signal classification techniques, while the CU estimate DoAs via minimum mean square error and MUSIC techniques. Further, we derive the closed-form Cramér-Rao Bound (CRB) expressions for estimated CU/target DoAs and establish the relationship between channel station information (CSI) error and CRB. In stage II, the DFBS transmits ISAC signals and maximizes the minimum effective signal-to-interference-plus-noise ratio (SINR) of CU by jointly optimizing two stage resources, while meeting sensing performance requirements and accounting for the impact of imperfect CSI. Since the complex interactions and strong coupling among variables, the formulated problem is non-convex and difficult to be solved directly. To address this issue, we begin by employing one-dimensional search to determine the sensing duration of Stage I. Then, based on this result, the DFBS beamforming optimization design is carried out with S-procedure method, penalty-based and successive convex approximation algorithms to convert the original problem into a tractable convex optimization problem. Finally, simulations are executed to confirm the advantages and effectiveness of our developed scheme.

Emerging wireless applications pursue a paradigm shift towards the integrated system that is capable of secure data transmission and high-resolution sensing in near-field environments. Conventional far-field use-cases suffer from the fundamental limitations in security, spatial precision, and spectral coexistence. Against this backdrop, this paper investigates an integrated covert communications and sensing (ICCS) system operating in the near-field environment. Specifically, the transmitter (Alice) aims to covertly convey messages to legitimate receivers (Bobs), while circumventing the detection by the eavesdropper (Willie) as well as improving the sensing performance at the target. To elevate communication performance, we aim to maximize the achievable sum rate to jointly optimize the communication and sensing beamforming matrices at Alice. The optimization problem is subject to multiple constraints with coupled variables: the transmit power budget at Alice, the minimum communication rate requirements for Bob, the Cram$\acute {e}$r-Rao bound (CRB) constraint to ensure accurate parameter estimation in sensing, and the covertness constraint against Willie’s detection. Given the non-convex nature of the formulated problem, an efficient successive convex approximation and semidefinite relaxation algorithms are proposed. In addition, we provide a theoretical analysis to confirm the convergence behaviour of the proposed algorithm, which can achieve the near-optimal solution. Finally, the numerical results are presented to highlight the superiority of the proposed ICCS system over existing counterparts. These results numerically verify the effectiveness of the proposed approach in enhancing communication rates while maintaining sensing performance and covertness in the near-field regime.

The distributed network architecture allows cell-free massive MIMO(mMIMO) with multiple ways to combine uplink signals. The decision feedback scheme uses the detected signal of the distributed combiner to estimate the equivalent channel at the central processing unit (CPU), and iteratively updates the symbols using the estimated channel state information (CSI). The performance of this algorithm greatly depends on the accuracy of initial signal detection. In this paper, we first propose a pilot-based signal combining (PC) scheme at the CPU to improve the estimation accuracy of the symbols. We use pilot signals instead of detected signal to estimate the equivalent channel at the CPU. The estimated data symbols is then used as the training input in the decision feedback scheme, enabling iterative refinement of the equivalent CSI. Simulation results show that the proposed PC scheme can significantly reduce the system bit error rate compared to the decision feedback scheme with lower computational complexity and less decoding latency, albeit with slightly increase fronthaul signaling load. Furthermore, continuing the iterative process with the decision feedback scheme after initial PC further reduces the bit error rate, albeit with added decoding latency and computational complexity similar to the standard decision feedback scheme.

Two nascent technologies, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS), present promising avenues to enhance spectral and energy efficiencies within multi-antenna frameworks. However, passive RIS may encounter challenges in delivering substantial capacity gains due to the cumulative path loss effect. Active RIS (ARIS) equipped with low-cost amplifiers in the reflective elements emerges as a solution to mitigate the limitation. This paper investigates a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) framework, augmented by an ARIS and leveraging RSMA. The primary objective is to maximize the system's SE, subject to constraints imposed by the design of BS beamforming vectors, PS ratios, and RIS phase shifts. To address the inherent nonconvexity of this optimization problem, we propose an innovative approach that combines alternating optimization (AO) and semidefinite relaxation (SDR) algorithms. Simulations results demonstrate the significant advantages of our proposed design over established benchmarks.

In this paper, we study the beamforming design in near-field integrated sensing and covert communication systems, where the transmitter (Alice) covertly sends information to a legitimate user (Bob) and senses the target concurrently while hiding from illegal eavesdropper (Willie). Considering a perfect Willie-involved CSI scenario, we propose a beamforming optimization problem to minimize the Cramér-Rao bound for sensing parameters under the conditions of total transmit power, the minimum communication rate and covertness constraint. For this optimization problem, the global optimal solution is obtained by using the semidefinite relaxation (SDR). Compared with the near-field integrated sensing and communication (ISAC) systems without covert constraint, the numerical results verify the effectiveness and feasibility of the proposed scheme.

Efficient UAV navigation in large-scale six-generation (6G)-enabled green internet of things (GIoT) systems presents significant challenges due to stringent energy constraints, dynamic network conditions, and the need for robust connectivity and high coverage. Existing methods often neglect critical factors such as real-time adaptability to network variations, scalable multi-agent coordination, and integrated communication-energy optimization. To address these gaps, this paper proposes a novel multi-agent UAV path-planning framework based on the proximal policy optimization (PPO) algorithm, explicitly designed for connectivity-aware navigation. The approach integrates simultaneous wireless information and power transfer (SWIPT)-based energy harvesting, dynamic obstacle avoidance, and communication-driven reward shaping to enable sustainable, efficient UAV operations. Extensive simulations in a custom Gymnasium environment, modeled on precision agriculture, validate the framework. Experimental results with three UAV agents demonstrate 100% mission success, up to 0.0096 J/bit energy efficiency, average communication latency below 11 ms, and improved coverage with zero collisions for two agents. Compared to classical planners (A*, Dijkstra) and baseline PPO methods, the proposed model achieves a 15% higher cumulative reward and superior energy-latency trade-offs. Fully decentralized decision-making further enables scalable, practical deployment. This framework enables real-time UAV coordination by integrating 6G communication, energy, and dynamic environment adaptation for Green IoT.

Backscatter communication emerges as a promising solution for green Internet of Things (IoT). Current research mainly focus on On-Off Keying (OOK) and Binary Phase-Shift Keying (BPSK) modulation schemes and most overlook the crucial aspect of tag circuit sensitivity. In this paper, we derive the capacity of the M-ASK modulated backscatter system under the circuit sensitivity constraint of the tag. We conducts a comparative analysis of capacity performance across three scenarios: (i) backscatter communication systems without sensitivity constraints, (ii) backscatter systems employing different modulation orders, and (iii) conventional point-to-point communication systems. It is found that the circuit sensitivity of the passive tags has a considerable impact on the system capacity. Furthermore, high-order modulation can effectively increase the channel capacity of the backscatter communication systems.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) introduces revolutionary capabilities by reaching full space coverage for wireless signals, significantly enhancing the efficiency and reliability of Internet of Things (IoT) networks compared to traditional RIS. In this article, we propose a novel framework that leverages STAR-RIS into wirelessly powered federated learning (FL) networks with a multiantenna access point, aiming to minimize system latency. A multivariable nonconvex optimization problem is formulated to optimize phase shift vectors of STAR-RIS, beamforming matrices, time, power, and computation frequency for each user in all phases of FL. Block coordinate descent (BCD) over the combination of an 1-D search algorithm and interior point method is employed to optimize time, power, computation frequency, phase shift vectors of STAR-RIS, and active beamforming matrix in the uplink transmission phase, while semi-definite relaxation via BCD addresses phase shift vectors of STAR-RIS and beamforming matrices optimization in harvesting and downlink transmission phases. On this basis, the optimized downlink transmission time and power are derived. The convergence of the proposed algorithm and the superiority of its performance compared to benchmark schemes are validated through comprehensive simulations. Our findings indicate the potential of FL, multiantenna aggregation server, and STAR-RIS in ushering in a new era of intelligent and efficient IoT networks.

This paper addresses the challenge of optimizing multi-hop routing in IoT-based digital twin systems, referred to as the MOUNTAIN problem. Multi-hop routing is inherently complex due to the need to balance energy consumption and communication reliability across multiple nodes, especially in dynamic and large-scale IoT networks. In MOUNTAIN, multiple IoT devices in the physical network (PN) frequently transmit data to the digital network twin (DNT), managed by a central server. Given the limited energy resources of IoT devices, our approach considers both energy efficiency and communication reliability. We formulate the MOUNTAIN problem as an optimization task aimed at reducing overall energy consumption while maintaining robust data transmission. Moreover, we address the problem with both single-task optimization and multi-task optimization and propose two corresponding evolution-based metaheuristics that utilize well-designed solution representations and genetic operators to obtain near-optimal solutions to the problem. Among them, the proposed Single-task Evolutionary Algorithm (STEA) solves each problem instance independently, while the proposed Multi-task Evolutionary Algorithm (MTEA) solves multiple instances at the same time to take advantage of exchanging useful solution information during parallel solution searches. Extensive experiments on synthetic datasets demonstrate that our proposed algorithms significantly outperform existing methods, reducing energy consumption and improving network stability. This research contributes to the development of sustainable and efficient IoT infrastructures, which are essential for the operational demands of digital twin applications.

In this article, we propose a novel integrated sensing and communication (ISAC) algorithm for massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with spatial-frequency wideband (SFW) effects. To obtain high accuracy of channel state information (CSI), the proposed algorithm initially utilizes a deep neural network (DNN) for channel estimation. Then, the estimated channel is expressed as a third-order low-rank tensor model, on which the canonical polyadic (CP) decomposition is performed to obtain three factor matrices. These factor matrices hold the information pertaining to channel parameters. By fitting the constructed tensor model, channel parameters, such as Angles of Departure (AoDs), Angles of Arrival (AoAs), time delay, and complex gains, can be extracted. Ultimately, the positions of mobile station (MS) and scattering points are determined by utilizing the mapping relationship between the channel parameters and position coordinates. In contrast to existing algorithms, the proposed algorithm delivers greater precision in both channel estimation and positioning. The simulation results demonstrate that the proposed algorithm maintains outstanding ISAC performance, persisting even with diminished compression rate. Furthermore, the proposed algorithm proves effective in more complex scenarios lacking a line-of-sight (LOS) path.

The demand for high-precision localization services has surged significantly due to the rise of intelligent transportation and autonomous driving in large-scale indoor factories. To mitigate the challenge of reduced positioning accuracy resulting from line-of-sight (LOS) occlusion in indoor visible light positioning (VLP) technology, this paper proposes a positioning strategy that leverages optical intelligent reflecting surfaces (IRSs) within indoor Internet of Vehicles (IoVs) environments. This scheme utilizes the principle of range positioning based on the time difference of arrival (TDOA). A weighted least squares (WLS) method is initially derived as a benchmark positioning approach based on TDOA. Additionally, two high-accuracy positioning methods, namely, the Chan method and Taylor series expansion method, are proposed for different scenarios. The Chan method provides a closed-form solution suitable for low-computation cases, while the Taylor series expansion method can be iteratively exploited in complex situations. The detailed procedures of these three positioning approaches are also presented. Furthermore, theoretical analyses of the computational complexity of the WLS method, Chan method, and Taylor series expansion method are provided. Additionally, the positioning performance of the Cramér-Rao lower bound (CRLB) is analyzed and derived for the considered system model. The simulation results illustrate that the system model and positioning methods outlined can asymptotically achieve the derived CRLB, thereby validating the efficacy of the proposed positioning scheme and methods. These advancements hold significant potential for industrial automation, logistics operations, and safety-critical autonomous guided vehicles (AGVs) in smart factories, where robust centimeter-level positioning is essential for collision avoidance and task coordination under dynamic occlusion conditions.

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

The rapid development and integration of wireless sensor networks (WSNs) in the consumer electronics industry signify an important shift toward more intelligent and interconnected technologies. The localization of network nodes is essential for promoting intelligent automation and effective decision-making processes across a wide range of consumer applications. To achieve more precise intelligent localization, this article proposes an algorithm based on Triple-Anchor Centroid and expected hop progress (EHP) weighting to estimate the distance between sensing devices (regular nodes) and gateway devices (anchor nodes). The algorithm is a range-free positioning scheme that combines the geometric constraints between two devices and the advantages of EHP. And it uses the geometric centroid of the shadow part formed by the triple-anchor point to estimate devices distance. Then, the devices distance is weighted based on the EHP estimation, and the experimental results show that the proposed method improves the accuracy of estimating distance. Finally, an improved quantum-adaptive gray wolf optimizer (QAGWO) is proposed to estimate the coordinates of sensing devices. The step factor is proposed to balance the global and local search capabilities to improve the search efficiency and performance of the algorithm. The simulation results show that the scheme is superior to the other algorithms in terms of positioning accuracy, and the application scenarios are more extensive.

As the Industrial Internet of Things (IIoT) expands, the number of stakeholders increases. Many entities require significant amounts of data from industrial devices. These data streams improve information sharing and optimize the industrial chain. However, IIoT’s enormous data volumes pose challenges to traditional encryption methods, which are unable to meet efficiency and energy consumption requirements. Thus, a new, efficient encryption algorithm is essential for managing data flows among IIoT subscribers. In this paper, we propose a fast, low-energy, high-security encryption scheme to manage multi-entity data streams. First, we introduce a novel encryption scheme based on the Subset Sum Problem (SSP), which improves energy efficiency and speed. Second, to meet subscription requirements, we employ attribute-based encryption (ABE) for key forwarding. Finally, we handle subscription revocations with device key updates. The device owner utilizes their secret value to generate an identity proof with a key update request.

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

In the evolving landscape of wireless communications, extremely large-scale multiple-input-multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This article investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multicandidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems.

This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate.

This article explores the benefits of deploying simultaneously transmitting and reflecting reconfigurable intelligence surfaces (STAR-RIS) in autonomous aerial vehicle (AAV) networks with simultaneous wireless information and power transfer. In the proposed system, the AAV utilizes STAR-RIS to radiate energy-carrying information signals (ECISs) to multiple outdoor energy receivers and multiple indoor information receivers without flying over indoor no-fly zone. Based on the AAV propulsion power formula, we successively introduce fly-hover-broadcast (FHB) and path discretization (PD) protocols to minimize the total AAV energy consumption by jointly using the extended penalty function and optimization algorithm based on the expected value of channel status information, where the AAV flight constraints, no-fly constraint, minimized energy or information threshold constraints, and STAR-RIS phase-shift constraints are met. The FHB protocol, which requires the AAV to radiate the ECISs to the users at only a few hovering positions, provides a lower bound performance of AAV energy consumption, while the PD protocol is used to discuss the general situation of the ECISs during AAV flight. Simulation results demonstrate that the utilization of the STAR-RIS in AAV networks outperforms the traditional RIS in improving energy efficiency and extending AAV flight time.

This article explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on disentangled information bottleneck is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier$\beta $in the classical information bottleneck (IB) framework is eliminated, which avoids the need to manually optimize$\beta $several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems.

We investigate the performance of an active reconfigurable intelligent surface (RIS)-assisted mixed radio frequency (RF)-terahertz (THz) relaying system, where the RF signal reaches the relay through the active RIS and is then transmitted to the user via the THz channel. Under this scenario, we analyze the system performance with the relay employing amplify-and-forward (AF) and decode-and-forward (DF) protocols. More specifically, we derive the exact expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both relaying protocols. Based on this, we obtain the exact expressions for the outage probability, average bit error rate (ABER), and average channel capacity (ACC). Furthermore, to gain deeper insights, we derive the asymptotic expressions at high SNRs and obtain diversity order (DO) of the system. Moreover, we extend the analysis to the variable gain relaying scheme. The findings reveal that the DOs for both relaying protocols are determined by the THz channel parameters, and the DO under the AF relaying protocol is twice that of the DF relaying protocol. Finally, we validate that active RIS (A-RIS) can more effectively assist the performance of the mixed RF-THz relaying system compared to passive RIS.

With the continuous development of Internet of Things (IoT), significant value has been generated, but numerous challenges remain. Recommender systems, as an effective tool to optimize IoT services, can significantly enhance user experience. However, the IoT’s demands for low latency and high-computational load make it difficult for traditional recommender systems to adapt. Moreover, traditional approaches often overlook the dynamic nature of user preferences, which are crucial for determining user satisfaction with IoT services. To address these issues, we propose a novel method called MoreGCN, which quantifies the temporal evolution of user preferences and integrates user interest modeling to accurately match similar users. This approach guides the learning of convolutional networks during the recommendation process. Deployed within a distributed computing framework and combined with meta computing, MoreGCN significantly improves computational efficiency and recommendation accuracy. Experimental results demonstrate that, across three benchmark datasets, MoreGCN consistently outperforms several existing state-of-the-art methods in terms of performance.

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

Wi-Fi is a very common means for providing wireless access to the Internet, e.g., using the 2.4GHz Industrial, Scientific, and Medical (ISM) band and more recently also the 6 GHz band via Wi-Fi 6E. Thanks to a chip recently launched by Semtech, in the same 2.4GHz band now can also operate Long Range (LoRa), which is widely used in Internet of Things (IoT) applications due to its low power consumption and wide coverage range. To allow for data interchange among these technologies, multi-radio gateways are needed, which introduce additional costs, complexities, and potential points of failure. To address this challenge, we propose the concept of Wireless to LoRa (WiLo) to make directional communication from Wi-Fi to LoRa. WiLo uses physical-layer (PHY) communication and dedicated input chips in the 2.4 GHz band to transmit information. To overcome the modulation technique differences between Wi-Fi and LoRa, WiLo leverages narrow-band communication, a technique that generates ultra-narrowband signals using single-tone sinusoidal signals by manipulating the payload of Wi-Fi devices. These signals can be detected by LoRa Wide Area Network base stations due to their high receiver sensitivity for long-range communication. Our experiments, which make use of both Universal Software Radio Peripheral (USRP) and commodity devices, demonstrate that WiLo can achieve concurrent wireless communication over a distance of 500 m, from commercial Wi-Fi chips to a LoRaWAN, with more than 96% frame reception rate. These findings show the effectiveness of WiLo in enabling reliable and efficient wireless communication over long distances, making it particularly relevant for applications such as remote monitoring systems, sensor networks, and smart cities.

In this paper, we investigate the joint data relay communication and semantic communication in an unmaned aerial vehicle (UAV)-based Metaverse system. Therein, UAVs as relays forward data from ground users to ground data collectors (GDCs). Meanwhile, they capture images of area of interests, and the images can be used to update digital twin (DTs) for Metaverse platforms. As the UAVs and their GDCs may belong to different platforms, they may use the same spectrum at the same time that cause interference to each other. A third party, i.e., a network service provider (NSP), is involved to provide licensed channels in terms of transmission periods to the UAVs. We design auction schemes as incentive mechanisms for trading the transmission periods between the UAVs and the NSP. With a single transmission period, we design a learning auction with neural networks constructed from the Myerson theorem that maximizes the NSP’s revenue while ensuring important economic properties. With multiple transmission periods, we develop a nearly-optimal auction scheme by using attention mechanisms. A semantic communication technique is implemented at each UAV to reduce the size of the original images and cost for using the licensed channels. Extensive experiments shows that the learning auction driven from the Myerson theorem outperform the baseline scheme in terms of NSP’s revenue and truthfulness, while the revenue obtained by the attention-based auction is much higher than the existing learning auction.

Existing channel estimation and data detection methods for faster-than-Nyquist (FTN) transmission over frequency-selective fading channels primarily face three key challenges: high pilot and guard interval overhead, low channel estimation accuracy, and long distances in satellite communication systems. To address the first two issues, we design a low-overhead frame structure based on circular convolution and, accordingly, propose a low-overhead precoding-driven channel estimation (PD-CE) algorithm. The proposed algorithm leverages circular convolution to suppress inter-block interference (IBI) from the channel with minimal guard intervals and eliminate FTN-induced IBI without guard intervals, significantly reducing pilot and guard overhead. Meanwhile, the limited guard interval mitigates noise enhancement, enabling PD-CE to achieve superior channel estimation accuracy over existing estimation methods. The third challenge arises from the imperfect channel state information obtained at the transmitter. To enhance the robustness in satellite communication systems, we design a precoding matrix based on the minimum mean square error (MMSE) criterion, introducing a low-overhead precoding-driven channel estimation and data detection (MMSE-PD-CEDD) algorithm for interference suppression. Simulation results indicate that, even under channel estimation error, the proposed MMSE-PD-CEDD algorithm exhibits superior interference resistance compared to existing algorithms, while its bit error rate performance loss remains within an acceptable range relative to the Nyquist criterion.

Irregularly spaced arrays are appearing in diverse ares, such as wearable devices, stealth aircrafts. This paper studies the two-dimensional (2D) direction-of-arrival (DOA) estimation issue for an irregularly spaced electromagnetic vector sensor (EMVS) array. An estimation method of signal parameters via rotational invariance technique (ESPRIT) approach is developed. Unlike existing ESPRIT-like algorithms, the proposed approach in this paper not only estimates the rough directional cosine waveform via the rotational invariance of the polarized response matrix, but also finds the refined directional cosine waveform via the rotational invariance of the spatial response matrix. This proposed algorithm is capable of offering closed-form analytics, thus greatly facilitating 2D-DOA estimation. Numerical results shown in this paper verify that the proposed approach outperforms existing ESPRIT-like algorithms at a sightly increased costs of computation. In addition, numerical results presented in this paper for the proposed 2D-DOA estimation approach also corroborate the theoretical derivations.

Given the rapid growth of diverse communication demands, future large-scale satellite-aerial-terrestrial integrated networks (SATINs) need to simultaneously provide services to users while guaranteeing spectral efficiency, secrecy and energy efficiency. This paper addresses the problem of maximising secrecy energy efficiency (SEE) in SATINs, which can accurately describe the effective trade-off between security, spectral efficiency and transmit power. Particularly, we investigate a secure beamforming (BF) scheme in cognitive SATINs that employs rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) in the presence of multiple eavesdroppers (Eves) in a UAV-aided secondary network (SN). To optimize the SEE for secondary vehicle users while satisfying the constraints of primary users (PUs), we utilize deep reinforcement learning (DRL) to address the coupling between different optimized parameters based on the improved long short-term memory proximal policy optimization (LSTM-PPO) algorithm. The main innovation of this paper is to design a sophisticated reward function, action space, and state space according to each constraint to speed up the convergence. In addition, simulation results show that the proposed DRL-based optimization scheme exhibits significant advantages in terms of SEE compared with benchmark schemes, validating the effectiveness of this work.

In order to reduce the impact of obstacles and improve system performance for traditional backscatter communication (BackCom) networks, we propose a reconfigurable intelligent surface (RIS)-assisted heterogeneous BackCom network framework, where multiple backscatter clusters share the spectrum resource with macrocell users in an underlay spectrum sharing mode and achieve self-sufficient energy of each low-power-consumption backscatter device (BD) via a radio-frequency energy-harvesting way. Then, a robust resource allocation problem with imperfect channel station information is studied under the constraints of the minimum rate requirement of each BD, the minimum energy requirement of each BD, the maximum interference power of each macrocell user, the reflection coefficient of each BD, and the phase shifts of each RIS. Moreover, based on the bounded channel uncertainty model, a max-min throughput resource allocation problem of multiple backscatter clusters is formulated by jointly optimizing the time allocation factors, the reflection coefficient of each BD, and the phase shifts of each RIS. To deal with the non-convex optimization problem caused by the uncertain constraints and non-convex constraints, the worst-case approach, successive convex approximation as well as semi-definite relaxation are applied. Finally, an iteration-based robust resource allocation algorithm is proposed accordingly. Simulation results demonstrate that the proposed algorithm has good fairness and stronger robustness.

In this paper, the sensing beam pattern gain under simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-enabled integrated sensing and communications (ISAC) systems is investigated, in which the dual-functional base station (DFBS) provides multiple targets sensing in the presence of environment clutters and communicates with multiple users simultaneously. However, multiple targets detection introduces new challenges, since the STAR-RIS cannot directly send sensing beams and detect targets, the DFBS is required to analyze the echoes of the targets. While the echoes reflected by different targets through STAR-RIS come from the same direction for the DFBS, making it difficult to distinguish them. To circumvent this issue, we first introduce the signature sequence (SS) modulation scheme to the STAR-RIS-enabled ISAC system, thus ensuring that DFBS can detect different targets through the SS modulated sensing beams. Next, via the joint beamforming design of DFBS and STAR-RIS, we develop a max-min sensing beam pattern gain problem, and meanwhile, considering the communication quality requirements, the interference limitations of multi-targets and clutters, the passive nature constraint of STAR-RIS, and the total transmit power limitation. Then, to tackle the complex non-convex problem, we propose an alternating optimization method to divide it into two sub-problems and iteratively solve them until convergence. For the former, by relaxing the rank-one constraint, the problem is transformed into the standard convex quadratic semi-definite program and can be solved through the semi-definite relaxation and semi-definite programming algorithms. For the latter, the penalty-based algorithm is used to convert the rank-one constraints as penalty terms to the objective function, and the successive convex approximation method is leveraged to solve it. Finally, simulation results are conducted to validate the benefits and efficiency of our proposed scheme.

An active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTAR-RIS)-aided rate-splitting multiple access (RSMA) system is investigated in this paper. Specifically, a multi-antenna base station (BS) employs the RSMA protocol to communicate with a covert user and a public user with the help of an ASTAR-RIS in the presence of hardware impairments. For this setup, the outage probability (OP) and the detection error probability (DEP) of the warden are derived to characterize the covert communication performance. The covert transmission rate ($C_{R}$) in high signal-to-noise ratio (SNR) regions is also taken into consideration, for which an accurate approximate expression is presented. Based on the analytical results, the influences of the number of ASTAR-RIS elements, the RSMA factors, and the reflection coefficient of the ASTAR-RIS elements are analyzed. Finally, numeric simulations validate the correctness of the theoretical results presented in this paper.

In this paper, a localization algorithm based on Bayesian tensor decomposition is proposed for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, which successfully achieves vehicle localization in intelligent transportation systems (ITSs). Considering that the FDA-MIMO radar array may suffer from unknown mutual coupling (UMC), the proposed algorithm first constructs selection matrices for elimination, and then models the received signals as a third-order complex-valued tensor. To reduce the computational complexity of tensor decomposition, real-valued processing and compression techniques are employed to transform the complex-valued tensor into a real-valued compressed one. Subsequently, the factor matrices are obtained by Bayesian tensor decomposition, from which the direction of arrival (DOA) and range of the vehicle are extracted. Finally, the vehicle location is determined through geometric relationships. Besides, the Cramér-Rao bounds (CRBs) for DOA and range are derived as a performance benchmark. The proposed algorithm is applicable to the manifolds of uniform linear arrays (ULAs) and uniform planar arrays (UPAs) with UMC. Unlike existing algorithms requiring prior knowledge of target numbers, the proposed algorithm realizes accurate vehicle localization under both known and unknown target numbers. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm.

The communication security and covertness of legitimate vehicles in the sixth-generation (6G) mobile communication intelligent automatic transportation systems (IATS) face significant challenges, as the communication equipment and wireless signals are complex and susceptible to information leakage. To address the above issues, this paper proposes a novel IATS that integrates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA). Concurrently, an eavesdropping vehicle, capable of monitoring and eavesdropping information from legitimate vehicles, is introduced to explore the joint security and covertness performance. A roadside device is also introduced to improve security and covertness performance by transmitting artificial noise. The closed-form expressions for outage probability (OP), detection error probability (DEP), and intercept probability (IP) are derived to characterize the reliability, covertness, and security of the proposed system. The impact of various system parameters on system performance has also been extensively conducted, including transmitted signal-to-noise ratio (SNR), interference power, the power allocation coefficients, and the number of elements in STAR-RIS. The simulation results reveal several key insights: 1) The OPs and IPs of the RSMA-IATS network gradually decrease and increase with the transmitted SNR, respectively; 2) Achieving an optimal balance in power allocation between monitoring and eavesdropping proves essential for effective eavesdropper management; 3) The energy efficiency (EE) of the proposed system exhibits dual peaks at higher STAR-RIS element numbers, contrasting with a single peak at lower element counts, underscoring the strategic deployment of STAR-RIS technology in RSMA-IATS network.

This paper researches the system evaluations of the reconfigurable intelligent surface (RIS)-assisted satellite aerial terrestrial integrated systems. To ensure the stability of the regarded network, a link selection scheme is presented to get the balance between the system performance and the system efficiency. Besides, in order to build a practical environment of the transmission networks, the imperfect hardware, channel estimation errors and co-channel interference are both considered in the networks. Relied on the above considerations, the detailed analysis for the outage behaviors is shown along with the asymptotic outage probability in high signal-to-noise ratio scenarios. Moreover, the diversity order and coding gain are also provided to give fast methods to confirm the system evaluation. Finally, some re-presentative simulations are provided to confirm the efficiency and advantage of analytical results and the proposed link selection scheme.

In this paper, we investigate the joint design of beamforming vectors and trajectory for unmanned aerial vehicles (UAVs) in integrated sensing and communications networks, aiming to maximize the achievable covert rate (ACR) for legitimate users against multiple passive wardens. Considering the worst-case scenario, where the wardens strategically select optimal decision thresholds, we derive the minimum detection error probability and incorporate covertness constraints within the beamforming scheme. Our approach entails formulating the design as a non-convex optimization problem for maximizing the average ACR along the UAV trajectory. The formulation takes into account various practical constraints, such as the maximum transmit power, UAV flight speed limitations, minimum beamforming gain towards sensing targets, and the detection probability threshold for wardens. To address this intricate problem, we propose a block coordinate descent-based optimization algorithm. This algorithm alternates between updating beamforming vectors and UAV trajectories, offering a high-quality suboptimal solution to the original problem. Theoretical analyses reveal that when the detection probability threshold is sufficiently small, a linear correlation emerges between the maximum relative variation ratio in the average received signal power at wardens under two hypotheses and the detection probability. Furthermore, to enhance covertness against the wardens, it is necessary to either decrease the projection of information beamforming covariance matrix or increase the projection of sensing beamforming covariance matrix onto the subspace spanned by the eavesdropping channel vectors. Finally, extensive simulations are presented to validate the covert performance enhancements of our proposed methodology, compared with various baseline schemes adopting existing approaches.

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) technologies are recognized for their programmability and high energy efficiency respectively, which will be the key parts of the future sixth generation (6G) mobile communication technology. The combination of the two technologies can improve communication security by reducing the probability of detection and decoding through enhanced transmission and backscatter transmission in different communication slots. In this paper, a dual-function RIS that supports cooperative relaying for covert communications is proposed. It operates in different communication slots (enhanced transmission slot and backscatter slot), but the performance is affected by phase errors due to function switching. A source covertly communicates with an intended destination via the help of RIS and cooperative relay. There is an illegal monitor aims to detect and eavesdrop the covert message. For this system, the outage probability (OP), intercept probability (IP), and detection error probability (DEP) in different communication slots are derived to examine the system reliability and security. Moreover, the system security probability (SSP) is proposed, and a block coordinated ascent (BCA)-based iterative algorithm is used to jointly optimize the power allocation coefficients to maximize the SSP. Simulation results show that increasing the number of elements can improve the security performance and mitigate the negative impact of RIS phase errors.

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.

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)-enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and low-frequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, spatial filtering and cyclic cross-correlation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cramér-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.

This paper proposes a secrecy enhancement scheme for the space shift keying (SSK) assisted multiple-input single-output (MISO) visible light communications (VLC) system in a complex indoor environment, where the line-of-sight (LoS) link of the transmitter and legitimate user can be blocked or exist. By leveraging a properly arranged mirror array as an optical intelligent reflecting surface (ORIS), a legitimate user can access confidential information, while an eavesdropping user cannot intercept the confidential message. To achieve this goal, an optical artificial noise (OAN) assisted secrecy enhancement strategy is introduced. In this strategy, the transmitter transmits both the desired signal and the OAN signal simultaneously while adhering to power and amplitude constraints. The average mutual information (AMI) and achievable secrecy rate (ASR) are employed to analyze the secrecy performance of the ORIS aided SSK VLC system. Furthermore, to adapt to different environments, four system configuration scenarios are presented, and the corresponding secrecy performance is analyzed. To clarify the theoretical results of the OAN assisted indoor MISO SSK VLC system with an ORIS, extensive simulation results are performed.

In this paper, we investigate covert communication in rate-splitting multiple access (RSMA)-assisted ambient backscatter communication (AmBC) systems. Specifically, in the presence of illegitimate users, a base station applies RSMA to the downlink transmission of covert users and common users in the the primary system of ambient backscatter. To evaluate the covertness of the system, we derive closed-form expressions for the detection error probability (DEP), outage probability (OP), and covert rate (CR), where the DEP is applied to evaluate the ability of the warden to detect the presence or absence of communication behaviors between the base station and the covert user. Based on these expressions, we also conduct an asymptotic analysis of the outage probability, an upper bound analysis of the covert rate, and an analysis of the impact of the power allocation factor on system performance. Numerical results show that the RSMA scheme has a higher covert rate and a higher minimum DEP value compared to the non-orthogonal multiple access (NOMA) scheme, but it may reduce its outage performance. Additionally, an appropriate use of the power allocation factor can enhance the covertness of the system. Furthermore, we find that the co-channel interference between the primary and secondary systems in the ambient backscatter system can degrade the covert performance of the primary system.

This paper researches the secrecy outage probabili-ty for the rate splitting multiple access-based integrated satellite-aerial-terrestrial networks. Specially, owing to some practical reasons, imperfect hardware is further analyzed for all the network nodes. Moreover, a UAV is utilized to help the signal transmitting from the satellite to the ground destination in the presence of an eve. Besides, by considering these limitations, the detailed analysis for the secrecy outage probability are gotten, which offer a good way to calculate the impacts of channel parameters and system factors on the secrecy networks. Finally, several representative Monte Carlo simulations are presented to confirm the rightness of the analytical results.

This paper investigates how to improve the energy efficiency of unmanned aerial vehicle (UAV)-enabled simultane-ous wireless information and power transfer (SWIPT) systems with multiple outdoor energy receivers (ERs) and multiple indoor wired-charging information receivers (IRs) by utilizing simul-taneously transmitting and reflecting reconfigurable intelligence (STAR-RIS), in which the UAV avoids flying over the indoor no-fly zone. Specifically, the total UAV energy consumption is minimized, while ensuring that the energy harvesting require-ment (EHR) of each ER and the communication throughput requirement (CTR) of each IR are met. To achieve this, the total UAV energy consumption is minimized by optimizing the STAR-RIS phase-shifts, the UAV trajectory, and hovering time using an iterative technique based on the fly-hover-broadcast (FHB) protocol. The technique allows the UAV to radiate energy-carrying information signals for the ERs and IRs at a limited number of hover positions. Simulation results demonstrate that the proposed design significantly outperforms other benchmark schemes, demonstrating its potential for improving the energy efficiency of UAV-enabled SWIPT systems while meeting the EHRs of each ER and the CTR of each IR.

This paper considers the scenario where multiple Internet-of-Things (IoT) devices collaborate to train a distributed model using federated learning. Wireless power transfer (WPT) is employed to address the issue of limited battery life of IoT devices, while non-orthogonal multiple access (NOMA) is utilized to facilitate data transmission. Besides, an intelligent reflecting surface (IRS) is applied to assist both energy transfer and data transmission. On this basis, a joint resource allocation problem is formulated to minimize the total energy consumption for the considered IRS-aided FL-WPT networks with NOMA. The non-convex problem is first solved by developing a combination of semi-definite programming relaxation (SDR) with a two-dimensional search algorithm. To lower the computational complexity, SDR with a bisection algorithm is further employed by exploiting the inherent structure of the formulated problem. Numerical results not only validate the equivalence of these two algorithms in performance but also unequivocally establish the superior efficiency of the proposed method over benchmark schemes in terms of energy consumption.

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

In this article, we consider an active reconfigurable intelligent surface (ARIS)-aided nonorthogonal multiple access (NOMA) and rate-splitting (RS)-enabled communication system, in which a base station applies RS and NOMA to the downlink transmission of a hidden user and a public user with the assistance of an ARIS in the presence of an illegal user. More specifically, the closed expressions for the outage probability (OP), covert rate, and detection error probability (DEP) are derived. Also, we present the analysis for the secrecy OP (SOP), and analyse the effects of detection threshold, total power consumption, reconfigurable intelligent surface (RIS) deployment distance, and power allocation factors on the system performance. The numerical results show that under the same total power consumption, the ARIS has a lower OP value than the passive RIS (PRIS), which can effectively overcome the “multiplicative fading” effect. Moreover, applying the ARIS can result in a larger covert rate and a higher minimum DEP value at the optimal detection threshold, and can reduce the SOP values of hidden and public users by reducing the power allocation factors. In addition, at the same minimum DEP value, the RS-NOMA scheme has a higher covert rate than the NOMA scheme.

Intelligent reconfigurable surface (IRS) is an innovative and promising technology to achieve intelligent reconfigurable wireless environment, and thus, enables cost-effective and energy-efficient wireless communications. Due to the broadcasting nature of the wireless signals, the reflected signal in IRS-assisted wireless communications networks might suffer from eavesdropping. Thus, it is essential to tackle the secrecy aware problems in IRS-assisted wireless communications networks. In this article, we consider an aerial IRS (AIRS) assisted wireless relay network scenario, where IRS is mounted on the aerial platform. The artificial noise is added to interrupt the eavesdropping. A secrecy rate maximization problem is formulated subject to the total transmit power and reflecting phase shift constraints. To solve this problem, we first divide the secrecy maximization problem into three subproblems, i.e., transmit power allocation, AIRS trajectory design, and reflecting phase shift optimization. These three subproblems are solved alternately until convergence to maximize the secrecy rate. Especially, for the AIRS trajectory design and reflecting phase shift optimization, we employ the successive convex approximation (SCA) and positive semidefinite relaxation (SDR) technologies to convert the nonconvex optimization problems into convex problems, respectively. The intercept probability of the proposed optimal schemes is derived and the theoretical analyses show that the intercept probability can be reduced by increasing the numbers of IRS elements. Simulation results show that the joint optimization of transmit power, AIRS trajectory and reflecting phase shift can effectively improve the secrecy rate.

Satellite and high-altitude platform (HAP) are considered as the key parts of the next generation networks, and specifically for the Internet-of-Things networks, which are utilized to provide unobstructed connections and massive user access for terrestrial networks. In this article, we investigate the power allocation (PA) and system performance of nonorthogonal multiple access (NOMA)-enabled integrated satellite-HAP-terrestrial systems under practical limitations. Particularly, a practical system model is established by considering the channel estimation errors and imperfect successive interference cancelation at the receiver. To achieve the different quality of service requirements among multiple served users, we propose a novel NOMA-based PA scheme. In addition, the analytical and asymptotic expressions for the outage probability of NOMA users are obtained to verify the proposed scheme as well as the ergodic capacity. Finally, numerical results are corroborated with Monte Carlo simulations, which show the correctness of our analytical results, and the benefits of our proposed scheme. The proposed scheme indicates that the HAP relay link plays a significant role in the system performance.

Reconfigurable intelligent surface (RIS) is playing a growing and ever-more significant role in constructing six-generation (6G) wireless networks due to its properties of low-cost and easy-integration. Current studies about RIS-assisted communication generally assume the RISs are deployed at fixed position or devices, however with the rapid development of unmanned aerial vehicle (UAV) technology, this readily flying wireless access platform is increasingly used to realize reliable communication. If the RISs are mounted on random 3-D (three-dimension) mobile UAVs, how to investigate the uplink transmission of RIS-assisted UAV communication systems would be a great challenge. To resolve this open issue, we establish a novel theoretical model to analyze the uplink performance of RIS-assisted UAV communication systems with random 3-D mobile pattern. In the modeling process, we firstly provide a random 3-D mobile model for UAVs, where the random waypoint and uniform mobility models are simultaneously used. Next we build an end-to-end (E2E) transmission model for RIS-assisted UAV communication system, where the impacts of channel fading type, RIS configuration, UAV’s mobility and association policies are comprehensively considered. Combining the above two models, we derive the analytical expressions of uplink transmission metrics, and make a bound performance analysis on this base. Finally, we evaluate the uplink performance of RIS-assisted UAV communication system with random 3-D mobile pattern, and verify the proposed theoretical model.

The dual-function radar communication (DFRC) system utilizes a hardware platform to achieve both radar and communication functions. In comparison to independently exploiting radar and communication systems, the DFRC system can significantly reduce system redundancy, volume, weight, and energy consumption. This makes DFRC an important area of research with practical value in advanced internet of things (IoT) techniques. This paper explores an exemplary DFRC system based on the orthogonal chirp division multiplexing (OCDM) methodology. In this system, OCDM achieves chirp multiplexing through the Fresnel transform and is identified as a potential replacement for OFDM in high-speed communication systems. In the proposed system, we integrate index modulation (IM) into the communication subsystem and utilize the subchirp index of OCDM to convey additional communication information, thereby significantly enhancing the communication rate of the DFRC system. Furthermore, a radar processing algorithm utilizing the OCDM signal is developed. This algorithm integrates the sparsity-aided compressed sensing (CS) algorithm into the radar subsystem to enhance estimation precision and reduce the sampling rate and hardware complexity of the radar receiver. Based on the proposed OCDM-assisted DFRC scheme, the communication rate of the DFRC system and the ambiguity function of OCDM are evaluated. The feasibility and effectiveness of the proposed DFRC system are confirmed through numerical calculations and simulation results.

Vehicle road cooperation systems are the vital components of intelligent transportation systems, destined to play an irreplaceable role in the future smart cites. Such systems are mandated to achieve elevated data rates, ultralow latency, and enhanced reliability. To meet these requirements, we incorporate full-duplex (FD) and rate splitting multiple access (RSMA) into vehicle road cooperation systems and propose a downlink FD RSMA vehicle road cooperation system. More importantly, we introduce a crucial metric to evaluate the influence of the delay constraints on the system performance. Specifically, analytical expressions for the effective capacity of the nearby vehicle and the distant pedestrian are derived. We also provide the approximate expressions for the effective capacity at the low and high-signal-to-noise ratios (SNRs) and the upper bound on the effective capacity to gain further insights. Furthermore, we expand our evaluation to include both throughput and energy efficiency for the FD RSMA vehicle road cooperation system. Results illustrate that: the effective capacity of the nearby vehicle increases with the increasing transmitted power at low SNRs and stabilizes at a constant level at high SNRs. Conversely, the effective capacity of the distant pedestrian increases continuously with the higher transmitted power. The effective capacities of the vehicle and pedestrian are influenced by various factors, such as the transmitted power, power allocation coefficients, and Quality-of-Service exponent.

The Internet-of-Things (IoT) has greatly facilitated our daily lives. Nevertheless, how to achieve higher spectral efficiency, large-scale device access, and lower latency for the next-generation IoT is still a challenge. Inspired by this, a non-orthogonal multiple access (NOMA) assisted cognitive radio (CR) IoT network is proposed in this paper, where the communication between the indoor secondary transmitter and secondary receivers is performed in the presence of an eavesdropper and under the constraint of secondary transmit power. In particular, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the secondary network to assist the secondary transmitter to communicate with its receivers in different rooms. To characterize the reliability and security of the proposed system, we derive analytical approximate expressions for the outage probabilitys (OPs) and intercept probabilitys (IPs) by using Gaussian-Chebyshev quadrature. With the aim of providing a deeper understanding, we also explore the impacts of transmission signal-to-noise ratios (SNRs), power allocation coefficient and the number of STAR-RIS elements on the system performance. Presented numerical results show that: 1) the OPs of near and far users gradually decrease with SNRs until floors appear at high SNR, and the floors of near user is always lower than that of far user; 2) IPs increasing with SNRs and near user is always less than far user, which proves that near user has better security; 3) under appropriate parameters, the trade-off between reliability and security of the considered system can be arisen.

This paper investigates the performance of physical layer security (PLS) in wireless communication systems, where a reconfigurable intelligent surfaces (RIS) is deployed between the transmitter and legitimate receiver to enhance the communication security. The Fisher-Snedecor F distribution is adopted to model the underlying fading channels, owing to its accuracy, tractability and generality. On this basis, this paper evaluates the performance of the proposed system by deriving the average secrecy capacity (ASC) and the secrecy outage probability (SOP) under correlated Fisher-Snedecor F channel coefficients. Furthermore, the asymptotic behavior of the ASC and SOP in the high signal-to-noise ratio (SNR) regime is examined. Analyzing the correlated scenario is crucial as it provides a detailed understanding of how interdependencies among channel coefficients impact the system’s security and overall performance, offering valuable insights into real-world communication scenarios. Finally, this paper verifies the analytical results through numerical illustrations, and demonstrates the effectiveness of employing RIS.

The number of mobile users and applications is rising quickly due to the deployment of fifth generation (5G) communication technology. The prevalence of smart devices and internet of things (IoT) services has made this technology essential to manage the resulting volume of data. However, the increase in mobile communications raises security concerns considering the open and dynamic nature of the industrial internet of things (IIoT) environment. An important research problem is how to exploit the characteristics of wireless channels for safe and reliable information transmission. Therefore, a mobile communication performance analysis and prediction algorithm based on FL-GLP-Net is proposed. First, a mobile security communication system model based on N-Nakagami channels is presented. Then, the non-zero secrecy capacity probability (NSCP) is studied and an exact expression is derived. XGBoost is used to choose the best features based on model performance. A real-time mobile security performance prediction model based on FL-GLP-Net is designed for real-time NSCP prediction. Federated learning (FL), graph attention network (GAT), long short-term memory (LSTM), and pyramid visual converter (PVT) modules are integrated to obtain a model that can deal with the diverse signal features in mobile communication systems. Results are presented to show that the proposed method outperforms other NSCP prediction algorithms. In particular, the mean squared error (MSE) of FL-GLP-Net is 78% better than that of FL-ShuffleNetV2.

Emerging technologies such as artificial intelligence and big data have made numerous Internet of things (IoT) applications possible. In particular, the Artificial Intelligence of Things (AIoT) has the potential to promote the digitization and intelligent connection of all things. However, the openness and diversity of AIoT makes data information vulnerable to security attacks which can lead to a disruption of mobile communication networks. The complexity of real-time data security events requires accurate prediction of AIoT security performance. In this paper, a secure communication system model based on decode-and-forward (DF) relaying is proposed and its security performance is analyzed. Expressions for the secrecy outage probability (SOP) are derived, and these are used to evaluate the security performance. For this purpose, an intelligent SOP prediction algorithm based on MS-Net is proposed. MobileNet and SqueezeNet networks are used to design an improved lightweight MS-Net model, which is composed of a depth separable convolution block and a fire module in parallel. The fire module is used to reduce the number of parameters in the first branch, and the depth-separable convolution block is employed in the second branch instead of the standard convolution. This can adapt to nonlinear characteristic in the AIoT safety data and reduce energy consumption. Afterwards, the convolutional block attention module(CBAM) attention mechanism is used to improve the model’s ability to capture features. The proposed algorithm provides better AIoT security performance than other algorithms. In particular, the mean squared error (MSE) is 68.1% better than that of RegNet.

In this article, we investigate covert communication in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted networks with rate-splitting multiple access (RSMA). Specifically, a legitimate transmitter employs RSMA to send messages over the STAR-RIS to a pair of legitimate users located on both sides of the STAR-RIS, where the user on the transmitting surface is equipped with a full-duplex (FD) receiver with two antennas. In particular, one of the receiver’s antennas is used to receive signals and the other is used to transmit jamming signals to confuse the warden’s detection. In order to evaluate the covert performance of the considered system, we derive closed-form expressions for the detection error probability (DEP), the outage probability, and the covert rate. Then, the effects of the maximum signal interference power of the FD, the power distribution coefficient, and the reflection coefficient of the STAR-RIS elements on the covert performance are presented. The numerical results indicate that increasing the maximum signal interference power of the FD enhances the value of DEP, but reduces its outage performance. Additionally, it is observed that the impact of the reflection coefficient of the STAR-RIS elements on the covert performance is related to the transmit power, and the DEP is a monotonically increasing function of the reflection coefficient when the transmit power is sufficiently high.

Integrated satellite-aerial-terrestrial relay networks (ISATRNs) have been considered as a promising architecture for next-generation networks, where high altitude platform (HAP) is pivotal in these integrated networks. In this paper, we introduce a novel model for HAP-based ISATRNs with mixed FSO/RF transmission mode, which incorporates unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) to dynamically reconfigure the propagation environment and fulfill the massive access requirements of ground users. Our aim is to maximize the system ergodic rate by joint optimizing the UAV trajectory, RIS phase shift, and active transmit beamforming matrix under the constraint of UAV energy consumption. To solve this intractable problem, a deep reinforcement learning (DRL)-based energy efficient optimization scheme by utilizing an improved long short-term memory (LSTM)-double deep Q-network (DDQN) framework is proposed. Numerical results demonstrate the superiority of our proposed algorithm over the traditional DDQN algorithm, on single-step exploration average reward values and other evaluation metrics.

In this paper, we study the performance of a cooperative underwater acoustic communication/free-space optical communication (UAC-FSO) transmission system supported by a fixed-gain amplify-and-forward (AF) relay, where an underwater source transmits signals to a satellite through the help of a buoy functioning as a relay located on the sea surface. In particular,Kand κ - μ shadowed fading distribution models are used to characterize the UAC link, while the FSO channel follows a unified Málaga distribution existing pointing errors. Based on the above considerations, we calculate the cumulative distribution function (CDF) and probability density function (PDF) of the end-to-end (e2e) signal-to-noise ratio (SNR). To assess the system performance, expressions for the outage probability (OP) and average bit-error rate (BER) are further obtained in closed-form. Furthermore, driven by a desire for more explicit insights, the high SNR analyses of the OP and average BER, and upper and lower bounds on the average capacity are presented. Finally, through Monte Carlo simulations, the correctness of the theoretical analysis is verified.

Remote Sensing Image Super-Resolution (RSISR) techniques play a crucial role in various remote sensing applications. However, deep learning-based methods applied to RSISR encounter difficulties in learning complex features of remote sensing images and modeling long-term correlations between pixels. This study proposes aMulti-Scale Sliding Window Attention Generation Adversarial Network (MSWAGAN), which combines the advantages of Convolutional Neural Networks (CNN) and Transformers to overcome these limitations. The MSWAGAN consists of three main parts. In the shallow feature extraction part, CNN is used to extract shallow features from remote sensing images. The deep feature extraction part is divided into two stages. Firstly, amulti-scale sliding window attention (MSWA)is designed to replace the multi-head attention (MHA) in the Transformer. MSWA can learn local multi-scale complex features of remote sensing images without increasing the number of parameters in MHA. Then, the Transformer is utilized to learn global image features and model the long-range correlations between pixels. The image reconstruction part utilizes sub-pixel convolution for feature upsampling. Furthermore, in order to extend the application of super-resolution remote sensing images, a cross-sensor real multi-spectral RSISR dataset consisting of Landsat-8 (L8) and Sentinel-2 (S2) images was constructed, and a series of experiments to improve the spatial resolution of L8 images from 30m to 10m in B, G, R and Near Infrared (NIR) bands were conducted. Experimental results demonstrate that our method outperforms some of the latest SR methods.

Internet of Medical Things (IoMT) has surfaced as the emerging era of the Internet of Things (IoT), drawing the attention of researchers given its broad operations in Smart Healthcare Systems (SHS) [1]. Since it is extremely risky for an individual to communicate with doctors in the hospital for every minor issue in the present pandemic scenario, we may check our day-to-day health records using IoMT devices and take precautionary measures on our own. In order to improve the delicacy, thickness, and outturn of electronic outfits, IoMT is essential to the healthcare sectors [2], [3].

In frequency selective channel environment, channel estimation in hybrid precoding millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a challenge issue. To solve this problem, we propose an effective channel estimation scheme for frequency selective channel, which is based on the training channel model in urban traffic environment. Considering that the practical mmWave MIMO channel is sparsity and the subcarrier multi-channels have the same sparse structure, we regard the channel estimation problem as the sparse channel recovery, and propose a multipath simultaneous matching tracking estimation method. It is assumed that the noise between the practical channels has a certain correlation, and the noise correlation has an impact on the selection of the optimal atomic support set in the process of channel recovery. Therefore, noise weighting is introduced in our proposed method. The simulation results prove the validity of this proposed method in frequency selective mmWave MIMO channel. Without increasing the complexity of the algorithm, the proposed method can achieve better local performance than the traditional classical methods.

The rapid expansion of Internet of Vehicles (IoV) networks has facilitated high throughput and reliable vehicular communications. Mobile vehicular networks face the challenges: diversification of network equipment, user mobility, and the broadcast nature of wireless channels, so physical layer security modeling of IoV communication systems has become important. The complexity of wireless communication channels makes real-time prediction of secrecy performance challenging. This paper presents an analysis of secrecy performance for mobile vehicular networks. To ensure data secure transmission, we have employed the decode-and-forward (DF) relaying scheme. The signal-to-noise ratio (SNR) of the effective end-to-end link is employed to obtain the mathematical expression results, which can evaluate the secrecy performance. The theoretical secrecy performance is confirmed via simulation. Then, we design a dense-inception convolution neural network (DI-CNN) model, and propose a DI-CNN-based intelligent prediction algorithm.Transformer, ShuffleNetV2, RegNet and YOLOv5 methods are employed to analyze the performance of DI-CNN algorithm. It is shown that the DI-CNN approach has a prediction accuracy that is 48.8% better than Transformer.

The intelligent transportation system (ITS) was supported by the sixth generation (6G) wireless networks, since it has great potential to realize intelligent transportation with the benefit for the society and economy. In order to overcome the practical problem of spectrum scarcity, ultra-low latency, large-scale connectivity in ITS, we propose a cooperative multiple-input single output non-orthogonal multiple access (MISO-NOMA) for ITS with intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). An user fairness optimization problem is formulated to maximize the fairness rate of the vehicles, subject to the quality of service requirements of the vehicles and the successive interference cancellation. The optimization problem involves the transmit beamformers design, the IRS reflection matrix design, and the power splitting ratio of the SWIPT, which lead to the problem is difficult to solve. For solving the challenging problem, an iterative successive convex approximation and semi-definite relaxation based algorithm is proposed. Explicitly, we firstly adopt the method of reconstructing epigraph for simplification due to the objective function is non-convex, and then the original problem is decomposed into two sub-problems that are easy to solve. Finally, Experimental results illustrate that the user fairness of the proposed cooperative MISO-NOMA for ITS with IRS and SWIPT is better than that of both the IRS-NOMA for ITS without SWIPT and the IRS-OMA for ITS.

In this paper, we consider a simultaneously transmitting and reflecting reconfigurable intelligent surface (STARRIS)-assisted Internet of Vehicles non-orthogonal multiple access network. For practical considerations, the impacts of residual hardware impairments and imperfect channel state information are investigated. For such a setup with three different protocols, including time switching (TS), energy splitting, and mode switching (MS), we present the outage probability (OP) analysis for the network. Moreover, the probability of signal-to-noise ratio (SNR) gain and the delayed outage probability are further investigated. Compared with the traditional relaying scheme, the obtained results show that the STARRIS-assisted system achieves better delay performance. Furthermore, for the STARRIS-assisted system, the TS protocol achieves the best performance, while the MS protocol has the worst performance. In addition, considering the base station with multiple antennas, the expression for the OP is derived and error floors at high SNRs due to imperfect channel state information constraints exist. Finally, one can readily observe that increasing the number of STARRIS elements and antennas of the source can improve the outage performance.

The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter.

In this paper, an effective simultaneous channel estimation and sensing algorithm is proposed for millimeter wave (mmWave) multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm consists of a two-stage channel estimation scheme and a reliable sensing scheme, which enables high-quality channel estimation and precise sensing in three-dimensional (3D) space. Specifically, the proposed algorithm first puts forward an improved simultaneous orthogonal matching pursuit algorithm that utilizes structural relation between the sparse basis and indexes to implement coarse estimation of multiple parameters. Subsequently, taking the obtained coarse parameters as initial values, optimization of channel parameters is achieved using the idea of maximum likelihood and gradient descent algorithm. Finally, we develop a reliable sensing scheme to realize user localization and mapping of scattering environment in various scenarios. Cramér-Rao bounds (CRBs) of the parameters and positions are also derived and used as a benchmark in simulations. Simulation results demonstrate that compared with the existing algorithms, the proposed algorithm has better channel estimation and sensing performance and is closer to CRBs. Moreover, even in challenging environment with unknown user orientation and clock bias, the proposed algorithm can achieve precise user localization and mapping of scattering environment.

To overcome the long transmission distances and limited spectrum resources issues, both the space-aerial-terrestrial relay networks (SATRNs) and hybrid-free space optical/radio frequency (FSO/RF) mode have attracted significant attentions. Specifically, high-altitude platform (HAP) and unmanned aerial vehicle (UAV) are employed in this paper to enhance the transmission reliability and improve the resource utilization along with the reconfigurable intelligent surface (RIS) and rate splitting multiple access (RSMA) techniques. Besides, we propose a novel access-free federated deep reinforcement learning (DRL) framework, which exploits the privacy-preserving security features of federated learning (FL) and DRL, to optimize active beamforming vectors, RIS reflection coefficients, UAV trajectory, and power splitting ratio. The learning process of the algorithm is performed locally which significantly reduces the computational overhead compared to traditional algorithms. Simulation results demonstrate that the proposed federated DRL-aided framework achieves higher energy efficiency compared to the reference schemes.

In this work, a mixed underwater acoustic/optical wireless transmission system under both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols is proposed. Assuming pointing errors and both heterodyne detection (HD) as well as intensity modulation/direct detection (IM/DD) techniques in the underwater optical link, we deduce the exact analytical formulas for the outage probability (OP), average bit error rate (ABER), and average capacity of the system under consideration. Also, we further derive the corresponding asymptotic expressions to gain intuitive physical insights about the system and channel models under consideration. Additionally, we extend the analysis to a more general multi-sensor system. Finally, we check the analytical results by Monte Carlo simulations.

In this paper, we investigate an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted integrated sensing and communications (ISAC) system, where the dual-functional base station (DFBS) operates in full-duplex (FD) mode to provide communication services and performs targets sensing simultaneously. Meanwhile, we consider multiple targets and multiple users scenario as well as the self-interference at the FD DFBS. Through jointly optimizing the DFBS and active STAR-RIS beamforming under different work modes, our purpose is to achieve the maximum communication sum-rate, while satisfying the minimum radar signal-to-interference-plus-noise ratio (SINR) constraint, the active STAR-RIS hardware constraints and the total power constraint of DFBS and active STAR-RIS. To tackle the complex non-convex optimization problem formulated, an efficient alternating optimization algorithm is proposed. Specifically, the fractional programming method is first leveraged to turn the original problem into a more tractable one, and subsequently the transformed problem is decomposed into several sub-problems. Next, we develop a derivation method to obtain the closed-form expression of the radar receiving beamforming, and then the DFBS transmit beamforming is optimized under the radar SINR requirement and total power constraints. After that, the active STAR-RIS reflection and transmission beamforming are optimized by majorization minimization, complex circle manifold and convex optimization techniques. Finally, the proposed schemes are conducted through numerical simulations to show their benefits and efficiency.

The trend of integrated satellite-HAP-ground networks (IS-HAP-GNs) as an critical directions for the future development of next generation network technology is widely recognized by academia and industry. Besides, utilizing reconfigurable intelligent surfaces (RIS) as a green paradigm, unmanned aerial vehicles (UAVs) can be equipped to reflect uplink signals from vehicle transmitters (VTs) to high altitude platforms (HAPs). Acting as relays, HAPs then forward these signals to satellites via hybrid free-space optical (FSO) links to enable rapid link deployment. In this paper, we firstly investigate a novel uplink signal transmission mode to maximize the system ergodic sum rate. Then, to tackle the high-dimensional non-convex optimization problems, we propose an asymmetric long short-term memory (LSTM)-deep deterministic policy gradient (DDPG) (AL-DDPG) algorithm builds on the deep reinforcement learning (DRL) framework. The numerical results demonstrate the superiority of the AL-DDPG algorithm over traditional optimization algorithms and reveal the effect of different system parameter settings on the performance.

To achieve low-cost, low energy consumption green Internet of Things (IoT) communication and meet 360oarea full-coverage, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted cognitive radio (CR)-non-orthogonal multiple access (NOMA) network. Specifically, the secondary transmitter serves as a relay of the primary network to forward the messages, and the secondary transmitter communicates with the user with the assistance of the STAR-RIS. To evaluate the performance of the considered network, we derive the outage probability (OP) for the users under the Nakagami-m fading channels. In addition, the asymptotic behavior at high signal-to-noise ratio (SNR) regions is analyzed. The following meaningful insights are obtained from the simulation experiments: 1) The OPs of users decrease continuously with the transmit power Ps, and increasing Psat high SNR is no longer effective for system reliability; 2) The increase in the components number of STAR-RIS has a positive impact on the reliability for the STAR-RIS assisted overlay CR-NOMA network and saturates after a certain value; 3) The scheme we considered has superior reliable performance by comparing with orthogonal multiple access.

This paper proposes an effective algorithm for gain-phase error (GPE) and angle estimation in multiple-input multiple-output (MIMO) radar. First, the received signals are constructed into a third-order tensor model containing information such as GPE in transmitting arrays (Tx) and receiving arrays (Rx), directions-of-departures (DODs) and directions-of-arrivals (DOAs). Then, a tensor-based two-stage estimation algorithm to estimate GPE and angles is developed. In the first stage, GPE and angle information are obtained by the least squares Khatri-Rao factorization (LS-KRF) separation. In the second stage, the GPE in Tx and Rx is estimated by a two-step GPE estimation scheme, while the DODs and DOAs are estimated by a non-iterative angle estimation scheme based on the spatial smoothing preprocessing. Simulation results show the superiority of the proposed GPE and angle estimation algorithm.

This paper investigates the covert millimeter wave (mmWave) communication in the finite block-length regime, where spatially random wardens attempt to determine the presence of transmission. First, we derive a novel expression of covertness constraint by using the tools of stochastic geometry, based on which the expression of average effective covert throughput (AECT) is also presented. Then, considering the constraint of maximal available block-length, the optimization problem for maximizing the AECT is formulated, and the optimal transmit power and block-length are analytically determined. Our results show the superiority of our optimization in terms of AECT in contrast to the fixed block-length case, and the improvement is more significant when the density of wardens becomes large. Furthermore, the performance of covert mmWave communication can indeed be improved via increasing the number of antennas even there exist random distributed wardens.

Millimeter-wave networks have already been successfully rolled out in many countries and now the research direction heads toward new technologies and standards to enable Tbps rates for future sixth-generation (6G) wireless communication systems. This work studies a point-to-point terahertz (THz) communication network exploiting the concept of a fluid antenna system (FAS) over correlated alpha-mu fading channels, nicely fitting the THz communication. Furthermore, the considered system is expanded to the selection-combining-FAS (SC-FAS) and maximum-gain-combining-FAS (MGC-FAS) diversity variates at the receiver side. The proposed FAS and its diversity configuration techniques are aimed to combat the high path loss, blockages, and molecular absorption effect related to the THz band. Our contribution includes comprehensive outage probability (OP) performance analysis for the THz band given the non-diversity and diversity FAS receivers. Moreover, the derived outage probability formulas are verified via Monte Carlo simulations. Numerical results have confirmed the superior performance of the MGC-FAS scheme in terms of OP. Finally, this work justifies that a higher number of antenna ports dramatically improves the system performance, even in the presence of correlation.

In this paper, we consider an Aerial Intelligent Reconfigurable Surface (AIRS) assisted wireless relay network, where IRS is implemented on the aerial platform, and the artificial noise is added to interrupt the eavesdropping. Based on the proposed system setup, it is formulated the secrecy rate maximization problem subject to the total transmit power and reflecting phase shift constraints. To solve this problem, we divide it into three sub-problems, i.e., transmit power allocation, AIRS trajectory optimization, and reflecting phase shift optimization. Alternately optimizing these three sub-problems, security aware joint optimal resource allocation schemes are proposed. Numerical simulations show that the joint optimal power allocation, AIRS trajectory, and reflecting phase shift schemes can effectively guarantee security performance for AIRS assisted wireless relay networks.

Smart cities intelligently control the functionalities of the city through the use of various electronic methods, sensors, and advanced communication techniques. An intelligent transport system (ITS) is the backbone of smart cities and refers to a system in which numerous vehicles utilize a communication infrastructure to exchange vital information, such as traffic, congestion, and road conditions. One of the key elements of ITS is vehicle tracking or localization, which is the monitoring of a moving vehicle’s location using a Global Positioning System (GPS). Accurate vehicle localization is required because many safety and traffic management applications depend on the precise positioning of the vehicles. Intelligent reflecting surface (IRS) is a new technology that offers attractive features like improved performance gains, enhanced network coverage, and flexible deployment, and is considered a key enabler for the 6G-driven vehicle-to-everything (V2X) systems that provide a programmable wireless environment. In this paper, a comprehensive view of a 6G-driven vehicle localization mechanism utilizing IRS is provided. The work also lists the benefits of IRS-enabled sensing in 6G vehicular networks including enhanced security, overcoming blockages, and improved localization. A case study to highlight the advantages of the IRS in vehicle tracking is presented. Finally, we also present the research challenges and future work directions in IRS-enabled vehicle tracking.

With the large-scale commercialization of 5G networks, the era of Internet of Things (IoT), which is oriented toward the Internet of Everything (IoE), is coming. Under the circumstance, reliable and secure communication are the great challenges for future wireless network because of the broadcasting characteristics of electromagnetic wave. Physical-layer security (PLS) is an effective way to ensure secure communication by exploiting random nature of fading channels. Motivated by this, we investigate PLS of the wireless-powered cooperative multirelaying for IoT networks in the presence of eavesdropper with the estimation errors of channel (EEC) and imbalance between in-phase and quadrature-phase (IIQ). Specifically, the relays can be charged by the source with the aid of energy harvesters, and a novel more secure modify-and-forward (MF) relay protocol is proposed. For further improving energy efficiency and reducing extra interference, the$K$th superior relay selection scheme is proposed since some best ones are not available due to some scheduling or failure. Based on the system under study, we derive the analytical expressions for the outage probability (OP), intercept probability (IP), and secrecy OP (SOP) to evaluate the reliable and secure performance of this consideration system. Particularly, we then analyze the asymptotic behaviors of the OP, IP, and SOP, respectively. Through computer simulation, we show that: 1) with EEC, the error floors of the OP and SOP are of presence; 2) multiple relays lead to better OP and SOP performance; and 3) IIQ improves the security and is detrimental to the system reliability.

Nonorthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems, are combined with relay and reconfigurable intelligent surfaces (RISs). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this article, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.

Cognitive radio and backscatter communication (BackCom) have been viewed as two promising technologies for the future green Internet of Things (IoT). The combination of these two technologies can not only enhance spectrum efficiency but also improve energy efficiency. However, most of the existing resource allocation (RA) algorithms in cognitive BackCom networks consider ideal channel state information and a time division multiple access protocol, which is unrealistic in practical systems and can not support the massive number of accessing users. To this end, in this article, we study a robust chance-constrained RA problem for nonorthogonal multiple access (NOMA)-based cognitive BackCom networks to overcome the influence of channel estimation errors and support for large-scale IoT nodes. Specifically, cognitive backscatter users (CBUs) can not only share the spectrum resource owned by primary users but also harvest surrounding radio frequency and transmit their own information over the primary signals. Moreover, CBUs can use the harvested energy to actively transmit information via an NOMA protocol. The robust RA problem with outage-probability constraints is formulated to maximize the total throughput of CBUs by jointly optimizing the transmission time, transmit power, and the reflection coefficients of CBUs. To tackle the nonconvex problem, the original problem is converted into an equivalent form by applying the linear objective function, an inequality transformation approach, and an auxiliary variable method. Then, an iteration-based RA algorithm is proposed to solve it. Simulation results demonstrate the effectiveness of the proposed algorithm by comparing it with the benchmark algorithms.

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm Optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment problem.

In the absence of high-resolution sensors, super-resolution( SR) algorithms for remote sensing imagery improve the spatial resolution of the images. Currently, most of the SR algorithms are based on deep learning methods e.g., convolutional neural networks(CNN). Particularly, the generative adversarial networks(GANs) have demonstrated accepted performances in image super-resolution owing to their powerful generative capabilities. However, remote sensing images have complex feature types, which largely limits the performances of GAN-based SR methods for real satellite images. To address this issue, an attention mechanism and a multi-scale structure are introduced into the generator of the GAN network, and a multi-scale attention GAN(MSAGAN) is constructed in this study. We sequentially arrange the channel attention module and the spatial attention module after the multi-scale structure to emphasize important information, suppress unimportant information details, improve the model’s performance. Furthermore, we add residual connections and dense blocks to further enhance the performance of the generative network by increasing its depth. We compared to other existing deep learning-based SR methods, our proposed MSAGAN algorithm performed better in generating high spatial satellite images.

As one of the key technologies to enable the internet of things (IoT), cloud computing plays a significant role in providing huge computing and storage facilities for large-scale data. Though cloud computing brings great advantages, new issues emerge, such as data security breach and privacy disclosure. In this paper, we introduce a novel secure and privacy-preserving outsourcing computing scheme (hereafter referred to as SPOCS) to tackle this issue. In SPOCS, the effective use of Intel SGX, one of the trusted execution environment (TEE), ensures the confidence and integrity of sensitive data in cloud computing and prevents data loss from causing privacy disclosure. In order to keep malicious cloud service providers (CSPs) from illegally tampering with the outsourcing results, blockchain is employed to ensure the data immutability. Significantly, our proposed scheme achieves anonymity and traceability. In the outsourcing process, smart contracts are applied to make the whole process fully automated without any human involvement. Finally, the security of the proposed scheme is analyzed in terms of its resistance to different attacks. The experiments indicate that our scheme is effective and efficient.

Ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA) are two promising technologies for the future wireless communication networks owing to their high energy and spectral efficiencies. The AmBC-aided NOMA symbiotic radio is a promising technology because of possessing advantages of AmBC and NOMA. Nonetheless, when a number of devices with limited power and computation capability access to the AmBC-based NOMA symbiotic networks, communication security becomes a critical issue. In this paper, we investigate physical-layer authentication (PLA) to identify the users and prevent illegal access and malicious activities for AmBC-based NOMA symbiotic networks. Moreover, channel estimation errors are considered when calculating the probability of false alarm (PFA) and probability of detection (PD) of the far user and near user. To enhance the authentication performance, three PLA schemes for the considered networks are designed according to the multiplexing form of the authentication tags: i) PLA with shared authentication tag (PLA-SAT); ii) PLA with space division multiplexing authentication tags; iii) PLA with time-division multiplexing authentication tags. To characterize the proposed PLA schemes, we first derive the PFA and the PD of the considered AmBC-based NOMA symbiotic networks. Then, the covertness is studied in terms of outage probability and asymptotic behavior in the high signal-to-noise ratio regime. Extensive analytical and computer simulated results show that: i) The PLA-SAT scheme has better performance than the other two authentication schemes with the same threshold; ii) The outage performance of systems employing authentication schemes is worse than those without authentication; iii) There exists a trade-off between robustness and covertness.

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

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

Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security.

The upcoming Intelligent Transportation System (ITS) supported by sixth generation (6G) communication technologies is expected to face the great challenges of spectrum scarcity, large-scale connectivity, ultra-low latency, and various security threats. To mitigate these challenges and implement the ITS in practice, we propose an overlay cognitive ambient backscatter communication non-orthogonal multiple access (ABCom-NOMA) network for the ITS. Specifically, we elaborate on the secrecy performance the overlay cognitive ABCom-NOMA based on ITS in the presence of an eavesdropping vehicle by deriving the secrecy outage probability (SOP) between the primary network, overlay secondary network, and the eavesdropping vehicle of the considered networks, respectively. For comparison, the secrecy performance of secondary receiving vehicles is taken into account, and a series of numerical simulations by Monte-Carlo methods are carried out to investigate the secrecy performance. From the numerical results yielded by the simulations, we can conclude: 1) The secrecy performance of the proposed the overlay secondary network is superior to the one of the primary network; 2) The increasing of the power allocation factor yields a positive effect on the secrecy performance of the primary receiving vehicles but a negative effect on that of the secondary receiving vehicles.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication (AmBC) play major roles to enhance spectrum efficiency in wireless communication systems. Besides, the AmBC provides good reinforcement for the current trend towards dispensing batteries for battery-free Internet-of-Things (IoT) devices. In this paper, we propose a symbiotic battery-free IoT system, that exploits the downlink transmission of a NOMA multiplexing enabled cellular network, to permit an IoT spectrum-efficient uplink communication. The IoT backscatter device (BD) performs a symbiotic radio (SR) relation with the cellular source to power its communication by intelligently reflecting the received power. We derive a closed-form expression of the ergodic capacity (EC) of the BD transmission and tight approximations of the ECs of the cellular source transmission, where all channels undergo Nakagami-m fading. Additionally, we validate the analytical results obtained using Monte-Carlo simulations. The influences of several system parameters such as power allocation factor, reflection coefficient, and channels’ severity factors have been investigated. Finally, the performance of the proposed system is compared with a benchmark OMA-based system to highlight the achievable performance improvement.

This paper investigates the performance of non-orthogonal multiple access (NOMA) based hybrid satellite-unmanned aerial vehicle (UAV) systems, where a low Earth orbit (LEO) satellite communicates with the ground users via a decode and forward (DF) UAV relay. We investigate a two NOMA users system, where a far user (FU) and a near user (NU) are served by the UAV which is located at a certain height above the origin of the coverage circle. The channel between satellite and UAV is assumed to follow a Shadowed-Rician fading and the channels between UAV and users are assumed to follow a Nakagami-m fading. New closed-form expressions of the outage probabilities for the two users and the system are derived. Different from other work in literature, we take into consideration different parameters affecting the total link budget. Additionally, we propose an algorithm for minimizing the system outage probability. The mathematical analysis is verified by extensive representative Monte-Carlo (MC) simulations. Finally, simulations are provided to demonstrate the impact of important parameters on the considered system as well as the superiority of the NOMA scheme the over reference scheme.

Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computer. To meet the demand for future internet traffic and mitigate the spectrum scarcity, this work presents the QA-aided maximum likelihood (ML) decoder for multi-user non-orthogonal multiple access (NOMA) networks as an alternative to the successive interference cancellation (SIC) method. The practical system parameters such as channel randomness and possible transmit power levels are taken into account for all individual signals of all involved users. The brute force (BF) and SIC signal detection methods are taken as benchmarks in the analysis. The QA-assisted ML decoder results in the same BER performance as the BF method outperforming the SIC technique, but the execution of QA takes more time than BF and SIC. The parallelization technique can be a potential aid to fasten the execution process. This will pave the way to fully realize the potential of QA decoders in NOMA systems.

Vehicle-to-everything (V2X) connectivity in 5G-andbeyond communication networks supports the futuristic intelligent transportation system (ITS) by allowing vehicles to intelligently connect with everything. The advent of reconfigurable intelligent surfaces (RISs) has led to realizing the true potential of V2X communication. In this work, we propose a dual RIS-based vehicle-to-infrastructure (V2I) communication scheme. Following that, the performance of the proposed communication scheme is evaluated in terms of deriving the closed-form expressions for outage probability, spectral efficiency and energy efficiency. Finally, the analytical findings are corroborated with simulations which illustrate the superiority of the RIS-assisted vehicular networks.

This article investigates intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency-division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this nonconvexity, the transmission time scheduling and the bandwidth allocation are optimally designed in the closed form by the Lagrange dual method and the Karush–Kuhn–Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose elementwise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in the closed form. We also characterize the convergence behavior of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

Given the explosive growth of portable devices, mobile crowdsensing (MCS) is becoming an essential approach that fully utilizes pervasive idle resources to accomplish sensing tasks. The traditional MCS relies on the centralized server for task handle is susceptible to a single point of failure. Targeting this security issue, researchers have proposed a series of blockchain-based MCS. However, nodes in the blockchain suffer from high computation cost for data processing. Simultaneously, most blockchain-based MCS systems lack an efficient incentive mechanism for service requesters and workers. In this work, we integrate the smart contract and mobile devices to establish a secure, interactive, and fair blockchain-based MCS system called BSIF. To prevent illegitimate participants, BSIF requests all users to verify their identities using private keys from the registration phase. In the case of worker location privacy leakage, the location-based symmetric key generator is adopted to coordinate a session key for target range worker selection. Besides, we transfer the data evaluation process to the requester side (e.g., a personal computer), reducing computation cost in the blockchain nodes. Due to the homomorphic feature of the Paillier Cryptosystem and common interest, the requester cannot violate the directives from the blockchain. Subsequently, the Stackelberg game is adopted to investigate the participation level of the workers and the fair reward mechanism for the requesters to achieve a dynamic balance. Finally, the security analysis and performance evaluation demonstrate that our BSIF can defend against possible adversaries while significantly cutting overhead and giving participants the utmost incentive.

The integration of artificial intelligence and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing. The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero secrecy capacity probability (NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this article, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the same convolution model. For the first two layers, they employ a 2 × 1 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.

Data transmission for the industrial Internet of Things (IoT) is crucial for industrial production, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. To ensure the continuity and robustness of industrial IoT communications in the case of damaged infrastructure communication facilities postdisaster, the industrial IoT can be connected with satellite networks in emergencies. This article presents a cross-regional, end-to-end, transmission control scheme for satellite-supported, multihop industrial IoT. The proposed scheme adjusts the window of data transmission from two phases, slow start and congestion avoidance, to accommodate the low-transmission performance caused by a long delay and high bit error rate in converged networks. The window of data transmission is also adjusted to increase the amount of data transmission for the slow start to fill the high bandwidth-delay product of the converged network, while adjusting the threshold of data transmission based on feedback information to distinguish different data losses during congestion avoidance. The feasibility of the heterogeneous network transmission model is experimentally verified. The results show that the scheme can achieve good performance in heterogeneous networks of industrial IoT and satellite networks. The scheme is effective in ensuring the continuity and stability of intelligent machine production in Industry 5.0 in emergency communication cases.

peer reviewed

With the help of the new generation information technology, the Internet of Vehicle (IoV) networks have become widespread. IoV can improve the automatic driving ability, and provide users with intelligence, comfort, safety, energy saving and efficiency traffic services. However, the IoV networks face serious challenges due to the complex wireless environment. The vehicles cannot obtain the real-time traffic condition and early warning information, which leads to the decrease of link quality and the failure of information transmission. To evaluate the communication quality of IoV networks, the outage probability (OP) is commonly employed as a metric. This paper considers mobile IoV networks, and investigates communication quality prediction. Novel OP expressions are derived, which can analyze the OP performance. Then, to predict OP in real time, an intelligent OP prediction approach with an Elman model is proposed. This is evaluated with data generated using the OP expressions. In terms of computational complexity and prediction accuracy, the results obtained show that the Elman-based approach provides better forecasting effect than other methods. For prediction accuracy, the proposed Elman approach is increased by 84.6%. For computational complexity, the execution time is reduced by 79.9%.

In this paper, we study a cognitive hybrid satellite-terrestrial relay network with imposed practical limitations, such as channel state information mismatch and transceiver-induced hardware impairments. Moreover, it is assumed that the network undergoes multiple independent and non-identically distributed interference noises arising from neighboring transmitters. Generalized closed-form expressions of the outage probability for a terrestrial user are obtained while taking into account the effect of an interference temperature constraint. Finally, analytical derivations are verified through Monte Carlo simulation and the impact of impairments is examined.

Massive connectivity and limited energy are main challenges for the beyond 5G (B5G)-enabled massive Internet of Things (IoT) to maintain diversified Qualify of Service (QoS) of the huge number of IoT device users. Motivated by these challenges, this article studies the performance of cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) for massive IoT systems. Under the practical assumption, residual hardware impairments (RHIs) and channel estimation errors (CEEs) are taken into account. The communication between the base station (BS) and two NOMA IoT device users is realized through a direct link and the assistance of multiple relays with finite energy storage capability that can harvest energy from the BS. Aiming at improving the system performance, an optimal relay is selected among K relays by using the partial relay selection (PRS) protocol to forward the received signal to the two NOMA IoT device users, namely, the far user (FU) and near user (NU). To evaluate the system performance, exact analytical expressions for the outage probability (OP) are derived in closed form. In order to get a better understanding of the overall system performance, we further undertake diversity order analyses by deriving asymptotic expressions for the OP in the high signal-to-noise ratio (SNR) regime. In addition, we also investigate the energy efficiency (EE) of the considered system, which is a crucial performance metric in massive IoT systems so that the impact of key system parameters on the performance can be quantified. Finally, the optimal power allocation scheme to maximize the sum rate of the considered system in the high SNR regime is also designed. Numerical results have shown that: 1) hardware impairment parameter has a deleterious effect on system performance while the channel estimation parameter is always beneficial to the OP; 2) the expected performance improvements obtained by the user of PRS protocol are enhanced by increasing the number of relays; and 3) the proposed power allocation scheme can optimize the sum-rate performance of the considered system.

The physical-layer security (PLS) of wireless networks has witnessed significant attention in next-generation communication systems due to its potential toward enabling protection at the signal level in dense network environments. The growing trends toward smart mobility via sensor-enabled vehicles are transforming today's traffic environment into Internet of Vehicles (IoVs). Enabling PLS for IoVs would be a significant development considering the dense vehicular network environment in the near future. In this context, this article presents a PLS framework for a vehicular network consisting a legitimate receiver and an eavesdropper, both under the effect of interfering vehicles. The double-Rayleigh fading channel is used to capture the effect of mobility within the communication channel. The performance is analyzed in terms of the average secrecy capacity (ASC) and secrecy outage probability (SOP). We present the standard expressions for the ASC and SOP in alternative forms, to facilitate analysis in terms of the respective moment generating function (MGF) and characteristic function of the joint fading and interferer statistics. Closed-form expressions for the MGFs and characteristic functions were obtained and Monte Carlo simulations were provided to validate the results. Approximate expressions for the ASC and SOP were also provided, for easier analysis and insight into the effect of the network parameters. The results attest that the performance of the considered system was affected by the number of interfering vehicles as well as their distances. It was also demonstrated that the system performance closely correlates with the uncertainty in the eavesdropper's vehicle location.

With the increasingly mature sensor technology and the increasing popularity of broadband network, “the Internet-of-Everything” era is coming, and the mobile Internet of Things (IoT) is booming around the world. However, the mobile IoT communication networks face serious challenges, which are caused by the complex and variable communication environments. The mobile IoT applications can produce large-scale data, which will consume substantial energy. The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks. To evaluate the performance of mobile IoT networks, it is important to analyze outage probability (OP) performance. In this article, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN). First, the mobile OP performance is analyzed by combining the TAS and decode-and-forward cooperative schemes, and the exact OP expressions are derived. Then, an improved CNN is designed to avoid the loss of important information, which contains the input layer, three-convolution layer, one fully connected layer, and output layer. The proposed CNN-based prediction approach is compared with the radial basis function (RBF), generalized regression (GR), Elman, and extreme learning machine (ELM) methods. The simulation results validate that the proposed CNN prediction approach can achieve a better prediction effect than RBF, Elman, GR, and ELM methods. For the CNN approach, it has a 44% increase in the prediction accuracy.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication have been envisioned as two promising technologies for the Internet-of-things due to their high spectral efficiency and energy efficiency. Motivated by this fact, we consider an ambient backscatter NOMA system in the presence of a malicious eavesdropper. Under the realistic assumptions of residual hardware impairments (RHIs), channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC), we investigate the physical layer security (PLS) of the ambient backscatter NOMA systems with emphasis on reliability and security. In order to further improve the security of the considered system, an artificial noise scheme is proposed where the radio frequency (RF) source acts as a jammer that transmits interference signals to the legitimate receivers and eavesdropper. On this basis, the analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived. To gain more insights, the asymptotic analysis and corresponding diversity orders for the OP in the high signal-to-noise ratio (SNR) regime are carried out, and the asymptotic behaviors of the IP in the high main-to-eavesdropper ratio (MER) region are explored as well. Finally, the correctness of the theoretical analysis is verified by the Monte Carlo simulation results. These results show that compared with the non-ideal conditions, the reliability of the considered system is high under ideal conditions, but the security is low.

This paper studies an Internet-of-Things (IoT) network employing a reconfigurable intelligent surface (RIS) over generalized fading channels. Inspired by the promising potential of RIS-based transmission, we investigate a RIS-enabled IoT network with the source node employing a RIS-based access point. The system is modelled with reference to a receiver-transmitter pair and the Fisher-Snedecor F model is adopted to analyse the composite fading and shadowing channel. Closed-form expressions are derived for the system with regards to the average capacity, average bit error rate (BER) and outage probability. Monte-Carlo simulations are provided throughout to validate the results. The results investigated and reported in this study extend early results reported in the emerging literature on RIS-enabled technologies and provides a framework for the evaluation of a basic RIS-enabled IoT network over the most common multipath fading channels. The results indicate the clear benefit of employing a RIS-enabled access point, as well as the versatility of the derived expressions in analysing the effects of fading and shadowing on the network. The results further demonstrate that for a RIS-enabled IoT network, there is the need to balance between the cost and benefit of increasing the RIS cells against other parameters such as increasing transmit power, especially at low SNR and/or high to moderate fading/shadowing severity.

This paper studies the physical layer security (PLS) of a vehicular network employing a reconfigurable intelligent surface (RIS). RIS technologies are emerging as an important paradigm for the realisation of smart radio environments, where large numbers of small, low-cost and passive elements, reflect the incident signal with an adjustable phase shift without requiring a dedicated energy source. Inspired by the promising potential of RIS-based transmission, we investigate two vehicular network system models: One with vehicle-to-vehicle communication with the source employing a RIS-based access point, and the other model in the form of a vehicular adhoc network (VANET), with a RIS-based relay deployed on a building. Both models assume the presence of an eavesdropper to investigate the average secrecy capacity of the considered systems. Monte-Carlo simulations are provided throughout to validate the results. The results show that performance of the system in terms of the secrecy capacity is affected by the location of the RIS-relay and the number of RIS cells. The effect of other system parameters such as source power and eavesdropper distances are also studied.

In this paper, we analyze the security capability of single-input multiple-output wireless transmission systems over k-μ shadowed fading channels in the presence of multiple eavesdroppers. Our security analysis relies on an important standard, i.e., average secrecy capacity which is more difficult and suitable for analyzing active eavesdropping scenario than secure outage probability and probability of strictly positive secrecy capacity. The novel expression of average secrecy capacity over k-μ shadowed fading channels with multiple eavesdroppers is deduced. The results of Monte Carlo simulation fully prove the correctness of our theoretical derivation. Through the obtained results, we observe that large antenna quantity in the highest signal-to-noise ratio regime, small number of the eavesdroppers, and small signal-to-noise-ratio of eavesdropping link will enhance confidentiality of the system under consideration.

This paper investigates the impact of residual transceiver hardware impairments (RTHIs) on cooperative nonorthogonal multiple access (NOMA) networks, where generic α - μ fading channel is considered. To be practical, imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) are taken into account. More particularly, two representative NOMA scenarios are proposed, namely non-cooperative NOMA and cooperative NOMA. For the non-cooperative NOMA, the base station (BS) directly performs NOMA with all users. For the cooperative NOMA, the BS communicates with NOMA users with the aid of an amplify-and-forward (AF) relay, and the direct links between BS and users are existent. To characterize the performance of the proposed networks, new closed-form and asymptotic expressions for the outage probability (OP), ergodic capacity (EC) and energy efficiency (EE) are derived, respectively. Specifically, we also design the relay location optimization algorithms from the perspectives of minimize the asymptotic OP. For non-cooperative NOMA, it is proved that the OP at high signal-to-noise ratios (SNRs) is a function of threshold, distortion noises, estimation errors and fading parameters, which results in 0 diversity order. In addition, high SNR slopes and high SNR power offsets achieved by users are studied. It is shown that there are rate ceilings for the EC at high SNRs due to estimation error and distortion noise, which cause 0 high SNR slopes and ∞ high SNR power offsets. For cooperative NOMA, similar results can be obtained, and it also demonstrates that the outage performance of cooperative NOMA scenario exceeds the non-cooperative NOMA scenario in the high SNR regime.

In this paper, a unified framework for 3-hop unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network is proposed. Aim at characterizing the performance of proposed framework, by using stochastic geometry, the analytical expressions for the outage probability of uplink/downlink transmissions are derived in closed- form for randomly deployed NOMA users. To obtain more insights of the network performance, the asymptotic analyses for the outage probability in the high signal- to-noise ratio (SNR) regime are carried out. These results reveal that for the uplink transmission, there exists an error floor due to the interference from the far user, while the performance of the far user can outperform the near one for the downlink transmission, which can be explained by the fact that the far user has a higher receiving power.

This paper investigates the secure performance of multi‐antenna decode‐and‐forward (DF) relaying networks where the Nakagami‐ m fading channel is taken into account. In practice, the joint impact of residual transceiver hardware impairments (HIs) and channel estimation errors (CEEs) on the outage probability (OP) and intercept probability (IP) are taken into account. Considering HIs and CEEs, an optimal transmit antenna selection scheme is proposed to enhance the secure performance and then a collaborative eavesdropping scheme is proposed. More specifically, they derive exact closed‐form expressions for the outage and intercept probabilities. To obtain more useful insights the asymptotic behaviours for the OP are examined in the high signal‐to‐noise ratio (SNR) regime and the diversity orders are obtained and discussed. Simulation results confirm the analytical derivations and demonstrate that: (i) As the power distribution coefficient increases, OP decreases, while IP increases; (ii) There exist error floors for the OP at high SNRs, which is determined by CEEs; (iii) The secure performance can be improved by increasing the number of source antennas and artificial noise quantisation coefficient, while as the number of eavesdropping increases, the security of the system is reduced; (iv) There is a trade‐off between the OP and IP.

This correspondence presents a jointly designed quasicyclic (QC) low-density parity-check (LDPC) coded-relay cooperation with joint-iterative decoding in the destination node. Firstly, a design-theoretic construction of QC-LDPC codes based on a combinatoric design approach known as optical orthogonal codes (OOC) is presented. Proposed OOC-based construction gives three classes of binary QC-LDPC codes with no length-4 cycles by utilizing some known ingredients including binary matrix dispersion of elements of finite field, incidence matrices, and circulant decomposition. Secondly, the proposed OOC-based construction gives an effective method to jointly design length-4 cycles free QC-LDPC codes for coded-relay cooperation, where sum-product algorithm- (SPA-) based joint-iterative decoding is used to decode the corrupted sequences coming from the source or relay nodes in different time frames over constituent Rayleigh fading channels. Based on the theoretical analysis and simulation results, proposed QC-LDPC coded-relay cooperations outperform their competitors under same conditions over the Rayleigh fading channel with additive white Gaussian noise.

We investigate the outage performance of cooperative non-orthogonal multiple access (NOMA) networks with transceiver hardware impairments, where the estimated channel state information and α - μ fading channels are considered. Closed-form expressions for the outage probability of cooperative NOMA networks are derived for two representative scenarios. The first scenario is the base station (BS) directly communicates with NOMA users. The second scenario is BS communicates with NOMA users via the AF relaying. The diversity orders are presented for the two scenarios. It reveals that there is an error floor for the outage probability due to the distortion noise and estimation error.

We consider a multi-pair two-way massive multiple-input multiple-out (MIMO) relaying system over Rician fading channels, where multi-pair users exchange their information via the amplify-and-forward (AF) relaying equipped with large number of antenna arrays. Hardware impairments at the relay and imperfect channel state information (CSI) are taken into account. More specifically, we derive a new linear minimum mean- square error (MMSE) channel estimator for the proposed system. It is demonstrated that normalized mean square error (NMSE) is a constant when the pilot power grows to infinity. Moreover, the asymptotic spectral efficiency with maximum ratio processing is presented in closed-form and the power scaling laws are analyzed. Simulation results indicates that massive MIMO is capable of compensating the loss caused by hardware impairments, estimation error and Rician fading.

In this study, the secrecy performance of the classic Wyner's wiretap model over shadowed fading channels is studied. More specifically, the authors derive two analytical expressions for the lower bound of secure outage probability at high signal‐to‐noise ratio regime and the probability of strictly positive secrecy capacity over shadowed fading channels, respectively. As there exist infinite series in the two derived expressions, they further obtain two simple and explicit approximate expressions for the lower bound of secure outage probability and the probability of strictly positive secrecy capacity with the aid of the moment matching method. It is shown that the match between the analytical results and simulations is very excellent for all parameters under considerations.

Small cell networks (SCNs) have emerged as promising technologies to meet the data traffic demands for the future wireless communications. However, the benefits of SCNs are limited to their hard handovers between base stations (BSs). In addition, the interference is another challenging issue. To solve this problem, this study employs a cooperative transmission mechanism focusing on correlated Rician/Gamma fading channels with zero‐forcing receivers. The analytical expressions for the achievable sum rate, symbol error rate and outage probability are derived, which are applicable to arbitrary Rician factors, correlation coefficients, the number of antennas, and remain tight across entire signal‐to‐noise ratios (SNRs). Asymptotic analyses at the high and low SNR regimes are carried out in order to further reveal the insights of the model parameters on the system performance. Monte‐Carlo simulation results validate the correctness of their derivations. Numerical results indicate that the theoretical expressions provide sufficiently accurate approximation to simulated results.

In this paper, we investigate the energy efficient optimization for non-regenerative multiple-input- multiple-output (MIMO) Two-Way Relay (TWR) network. Contrary to existing energy efficiency (EE) optimal precoding designs, we maximize the weighted sum per- source energy efficiencies (WSEE) subject to the maximum transmit power constraints of sources and relay, such that different EE requirements from the sources can be investigated. The resulting optimization problem is a non-convex sum of ratios problem. To address this issue, we first reformulate the objective as a more tractable parametric subtraction form by introducing some auxiliary variables. Then we develop an efficient block coordinate decent method to solve the equivalent problem with respect to the individual optimization variable. It is shown that the proposed iterative algorithm is guaranteed to converge.

Three-dimensional multiple-input multiple-output (3D MIMO) is a promising technology for the future wireless communication systems, since antenna tilt angle can reduce the intercell interference. In this paper, we consider 3D MIMO downlink analysis for a multicell multiuser scenario. Zero-forcing (ZF) precoding technique is applied at BS with perfect channel state information of all users in its cell. We derive the exact analytical expressions for downlink sum rate and outage probability for the multicell multiuser 3D MIMO system. Furthermore, we study the asymptotic system performance in the high signal-to-noise ratio (SNR) and large number of antennas regimes. The relationship between tilt angle and sum rate is given. By analyzing the effects of the tilt angle and distance between users and BS on outage probability, the optimal tilt angle and distance are determined to guarantee the outage probability performance.

In this paper, we provide a Gamma distribution to approximate composite Gamma-Gamma (Generalized-K or KG) distribution by using the moment matching method. Based on the approximate distribution, we propose a novel closed-form approximate upper bound of D-MIMO system. The proposed upper bound, which enable us to go further manipulations, only involves some simple functions. Starting from the approximate upper bound, we perform a high-SNR analysis and investigate the asymptotic behavior of the capacity in the following two cases: i) the number of receive antennas grows into infinity for the fixed average and total transmit power, and ii) the number of antennas at both ends grow large at a fixed ratio. It is demonstrated that the proposed approximate and the asymptotic upper bounds match accurately with the exact analytical expression.

At present, the joint utilization of the elevation factor in three-dimension (3D) channel model and antenna tilt angle in 3D base station is rarely investigated. In this paper, we consider the uplink of a multicell multiuser multiple-input multiple-output (MU-MIMO) system with a 3D base station exploiting variable antenna tilt angles. To illustrate clearly, we model a tall building with several floors. Users are distributed horizontally and vertically according some rules. Due to the storey height, elevations from one BS to different floors also differ, i.e. elevation variation occurs. For such a scenario, we derive the expression of sum rate considering the elevation factor, and discuss the effects of the tilt angle and user distribution on the sum rate. Sum rate at high signal-to-noise ratio (SNR) and large number of base station antennas is also studied. Finally, we analyze the relationship between the sum rate and the user number. This work can be seen as a first attempt to analyze the sum rate performance for high-rise buildings and used as a reference for infrastructure.