This paper investigates a novel pinching antenna assisted multi-target integrated sensing and communication (ISAC) system. We propose a joint optimization of transmit and receive PA positions to enable the simultaneous service of multiple sensing targets and a downlink communication user. The objective of the formulated optimization problem is to maximize the minimum sensing signal-to-interference-plus-noise ratio (SINR), which addresses inter-target interference and ensures fairness across multiple targets. The design jointly considers both the transmit and receive antenna positions as well as the receive beamformer. An efficient alternating optimization framework is introduced to handle this non-convex problem. The receive beamformer is obtained in closed-form using the minimum variance distortionless response method, while the antenna position optimization is solved via the differential evolution (DE) algorithm. The DE search process is guided by a customized fitness function that incorporates both communication quality-of-service requirements and physical constraints. Numerical results demonstrate that the proposed joint optimization scheme substantially outperforms transmitter-only optimization benchmarks, receiver-only optimization, and fixed antenna placements. Furthermore, comparative studies show that the DE algorithm achieves a better trade-off between performance and complexity, it not only surpasses particle swarm optimization in terms of performance, but also outperforms element-wise search when the antenna number is small, while maintaining lower computational complexity than both alternatives.

The non-orthogonal superimposed pilot (NOSIP) scheme significantly improves the spectrum efficiency of semantic communication (SemCom) systems by effectively reducing pilot overhead. However, existing SemCom systems based on NOSIP still face challenges, including strong model-channel coupling and limited adaptability to heterogeneous channels. To address these issues, this paper proposes a flexible, channel-adaptive digital SemCom (D-SemCom) architecture based on the NOSIP scheme. Specifically, we design a lightweight semantic codec, termed ShiftViT, and a semantic receiver, termed ShiftRx, which employ time- and frequency-domain shift mechanisms to decouple pilot and data and suppress multi-user interference, thereby enabling image transmission under complex channel conditions. Furthermore, a lightweight channel adaptation algorithm based on first-order meta-learning is proposed to facilitate rapid adaptation and mitigate the strong coupling between semantic models and channel environments. Numerical results demonstrate that the proposed D-SemCom system achieves approximately 25.14% and 1.16% goodput improvements over traditional receivers and existing state-of-the-art methods, respectively, while reducing the computational complexity in terms of FLOPs by approximately 40.37%. In addition, the proposed channel adaptation algorithm is shown to rapidly adapt to diverse channel scenarios within the D-SemCom system.

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.

This paper addresses the problem of capacity maximization in double reconfigurable intelligent surface (RIS) -aided multiple-input single-output (MISO) systems under multi-user scenarios. A system model is proposed for double RIS-aided MISO configurations, incorporating both passive RIS (PRIS) and active RIS (ARIS). The primary objective is to maximize the minimum channel capacity across all users, and a corresponding optimization problem is formulated to achieve this goal. To overcome the challenges posed by the fractional structure of the non-convex objective function, auxiliary variables are introduced, and the bisection method is utilized. Given the highly coupled and nonconvex nature of the optimization variables, a joint alternating optimization (AO) algorithm is designed based on the bisection method and successive convex approximation (SCA), enabling iterative determination of the beamforming vectors at the base station and the reflection coefficients of RISs. The results indicate that deploying double RISs substantially enhances the system’s channel capacity through reflected links. Notably, the ARIS protocols yield higher performance gains, particularly under scenarios involving high total power and large amplification factors. In multi-user environments, the double RIS configuration significantly outperforms the single RIS setup due to the increased degrees of freedom, especially when supported by ARIS protocols. These findings underscore the substantial potential and advantages of double RIS configurations in complex communication environments.

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.

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.

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.

A joint cascaded channel estimation scheme is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems with hardware imperfections. In particular, the practical hybrid near- and far-field electromagnetic radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations between different users and between different STAR-RIS elements, a multi-task learning (MTL)-based channel estimation framework is proposed. This framework is capable of estimating the cascaded channels for transmission and reflection simultaneously based on noisy observations of the mixture channel. Following the design guideline of the proposed MTL framework, an efficient multi-task network (MTN) is developed to reconstruct the high-dimensional channels with limited pilot overhead. In the proposed MTN architecture, a mixed convolution and multilayer perception module is exploited to capture the effective hybrid-field channel features. This module integrates the locality bias modeling of the channel-wise convolution and the long-range dependency modeling of MLP, which finely learns both local spatial correlations and specific spatial non-stationarity of the hybrid-field cascaded channels. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with the existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced simultaneous wireless information and power transfer (SWIPT) system is investigated, in which a multi-antenna access point (AP) provides communication service and power supply for multiple single-antenna information decoding receivers (IDRs) and energy harvesting receivers (EHRs), respectively. To fully investigate the potential of STAR-RIS, three types of STAR-RIS protocols are employed in the SWIPT systems, namely the energy splitting (ES), the mode switching (MS), and the time switching (TS) protocols. The weighted sum power maximization problem is formulated for each STAR-RIS protocol to maximize the sum power received at the EHRs by jointly optimizing the beamforming at the AP and STAR-RIS. The non-convex problem for each STAR-RIS protocol is handled by the proposed low-complexity Gaussian randomization-based and high-precision penalty-based joint optimization algorithms to provide a more comprehensive range of algorithmic choices. Furthermore, we demonstrated that the AP only needs to send the information beam to achieve the best power supply and communication service in adopting any STAR-RIS protocol. Finally, numerical results demonstrate that: 1) the proposed schemes can achieve higher sum received power compared to the benchmarks; 2) the sum power received at the EHRs of the three STAR-RIS protocols can be ranked as ES > TS > MS; and 3) the performance gap in sum power received between penalty-based and Gaussian random algorithms depends on the number of STAR-RIS elements.

A non-orthogonal multiple access (NOMA)-based spatial modulation system operating over multiple subchannels is investigated. For scheduled users of each subchannel, a mixed multicast and unicast transmission is delivered. The multicast content is transmitted via the transmit antenna domain, while unicast contents are transmitted through the amplitude-phase modulated symbols using NOMA via the active antenna. Firstly, the unicast rate for each user and an upper bound for the multicast rate are derived. Secondly, a joint subchannel and power allocation problem for weighted sum rate maximization is formulated. To solve this challenging mixed-integer non-linear problem, we decompose it into three subproblems, namely the decoding order design, the subchannel assignment, and the power allocation. A heuristic scheme is developed to solve the first one by investigating the characteristics of the decoding order constraint. To avoid the high complexity caused by exhaustive search, the subchannel assignment is reformulated as a many-to-one matching with peer effect, and the Gale-Shapley method and swap operation are designed to solve it. The power allocation is solved by employing the successive convex approximation. Moreover, a joint subchannel and power allocation algorithm is proposed to further boost the performance, and a robust power allocation algorithm is proposed under channel uncertainties.

Considering the charging needs of the Internet of Things, we introduce the simultaneous wireless information and power transfer (SWIPT) technology into the ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul. However, SWIPT can bring potential eavesdropping issues. In this paper, we study the secure communication caused by SWIPT in the UD-CRAN network. Specifically, the transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense networks, such as base station diversity and high probability of line-of-sight transmission. Aiming at maximizing the security energy efficiency, we jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of remote radio heads (RRHs), and user-RRH association. We propose an iterative algorithm based on the Dinkelbach’s transform to deal with the fractional objective function. To solve the mix-integer non-convex inner problem, we design: (1) a successive convex approximation (SCA) based method in which the problem at each iteration is a mixed-integer second-order cone program; (2) and an alternating optimization algorithm based on semidefinite relaxation (SDR) to further balance the complexity and performance. Finally, numerical results are presented to demonstrate the efficiency of the proposed schemes. Moreover, the proposed SCA method can achieve excellent performance while preserving integer variables, which inevitably increases algorithm complexity. Furthermore, the proposed SDR method can avoid the iteration process of SCA and further reducing the algorithm complexity.

A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

A multi-scale attention based channel estimation framework is proposed for reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output systems, in which hardware imperfections and time-varying characteristics of the cascaded channel are investigated. By exploiting the spatial correlations of different scales in the RIS reflection element domain, we construct a Laplacian pyramid attention network (LPAN) to realize the high-dimensional cascaded channel reconstruction with limited pilot overhead. In LPAN, we leverage the multi-scale supervision learning to progressively capture the spatial correlations of the cascaded channel, where the attention mechanism based dual-branch architecture is designed. To balance network performance and complexity of LPAN, we further propose a lightweight LPAN-L architecture. In LPAN-L, the partial standard convolutional layers are decomposed into the group convolution, dilated convolution and point-wise convolution, which forms a sparse convolutional filter set to extract the channel feature with less computation cost. Furthermore, we leverage parameter sharing and recursion strategy to reduce the space complexity. Moreover, a selective fine-tuning strategy is developed to realize the domain adaption. Simulation results show that the proposed LPAN can achieve higher estimation accuracy than the existing estimation schemes, while the LPAN-L architecture with a close performance to LPAN efficiently reduces the network complexity1.

In this paper, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) supported simultaneous wireless information and power transfer (SWIPT) system, in which a multi-antenna access point (AP) provides power supply and communication services for multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) respectively. To maximize the performance of the STAR-RIS, we utilize the energy splitting (ES) operation protocol in this paper. By optimizing the AP beamforming and the STAR-RIS transmission and reflection coefficients, we create a weighted sum-power maximization problem. We demonstrate that the AP can service both the IDRs and the EHRs by merely delivering information signals, which simplifies the initial optimization challenge. Then, the initial problem is split into two subproblems, and an alternative optimization method is utilized to produce high-precision suboptimal solutions to the optimization problem. Finally, we testify that the sum power received by EHRs is remarkably enhanced by the STAR-RIS-aided SWIPT systems compared with benchmark schemes in numerical results.

A joint cascaded channel estimation framework is proposed for simultaneously transmitting and reflecting recon-figurable intelligent surfaces (STAR-RIS) systems with hardware imperfection, in which practical the hybrid-field electromagnetic wave radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations in user domain and STAR-RIS element domain, we propose a multitask network (MTN) with multi-expert branches to simultaneously reconstruct the high-dimensional transmitting and reflecting channels from the observed mixture channel with noise. In the proposed MTN architecture, a learnable shrinkage module is exploited to constrict the communication noise, and self-attention mechanism-based Transformer layers are utilized to extract the nonlocal feature of the non-stationary cascaded channel. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

Motivated by the development of single image super-resolution (SR) reconstruction in computer version, classic SR networks have been widely applied to the channel estimation of wireless communication system. To capture the spatial correlations in the reflection element-domain of reconfigurable intelligent surface (RIS), we propose a multi-scale supervised learning-based Laplacian pyramid wide residual network (LapWRes) to achieve the progressive reconstruction of cascaded channel in a coarse-to-fine fashion. The LapWRes can be divided vertically into feature extraction branch (FEB) and channel reconstruction branch (CRB), while it can also be viewed horizontally as multiple channel reconstruction modules (RMs) at different scales. In the FEB, the wide activation residual blocks are stacked to extract the high-frequency information of cascaded channel. In the CRB, the high-frequency and low-frequency information of cascaded channel is fused by utilizing the residual learning. Simulation results show that the LapWRes can achieve better estimation accuracy than other channel estimation schemes and faster convergence than existing SR network-based channel estimation models.

Abstract In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS‐aided MEC system, where an access point services multi‐MEC devices by utilizing two distributed IRSs to operate a partial offloading strategy. Through collaboratively constructing the cooperative passive beamforming at the two IRSs, the central processing unit (CPU) frequency, the offloading time allocation, and the transmit power of users, an optimization problem is constructed to maximize the sum computation rate of the proposed system. To tackle this non‐convex issue, the authors first illustrate that the design of passive beamforming is independent to optimize other variables and propose to solve it alternatively. Then the joint optimization problem of other parameters is proved to be a convex problem and is resolvable by adopting the Lagrange dual approach. Compared with the benchmark schemes, the simulation outcomes demonstrated that the performance of the proposed system is vastly improved by deploying two distributed IRSs.

Abstract In this work, the authors consider the security performance for the mixed radio frequency link and visible‐light link cooperative relaying system, where the relay has the energy collection ability and can extract the direct current part existing in the visible‐light signal. Then, the relay uses the collected energy to demodulate the received visible‐light signal and retransmit it through the radio frequency link. With this equipment, some analytical expressions of the average secrecy capacity and the secrecy outage probability are derived. Furthermore, the asymptotic expression is also derived to examine the influence of the various configuration parameters on the secrecy performance of the mixed relaying system. At last, some numerical and simulation results are given to verify the theoretical analysis.

High-speed railway (HSR) wireless communications are required to ensure strict security. In this work, we study the secrecy performance of a nonorthogonal multiple access- (NOMA-) aided HSR wireless communication system in the case of an eavesdropping user. Specifically, applying NOMA technology to the HSR communication system can effectively improve the data rates. Therefore, we study the secrecy performance of the downlink NOMA system under the HSR wireless communication. In particular, the exact analytical results for the secrecy outage probability (SOP) based on no small-scale channel state information (CSI) are derived. We also provide all of the parameterizations for the proposed channel model. Finally, the correctness of theoretical derivation is verified via simulations. Results show the positive effect of utilizing the NOMA for enhancing wireless systems secrecy performance.

In this paper, the exact closed-form capacity expression for a hybrid selection/MIMO (H-S/MIMO) system in the spatial correlated Rayleigh fading channels is investigated. There are two assumptions in this work. One is that the antenna selection at the transmitter is based on the criterion that maximizes the received signal power and only one transmit antenna is chosen for a transmission interval. The other is that the spatial correlation is present at both ends of wireless communications link. Based on the two assumptions, the characteristic function (CF) method is used to analyze the capacity of the H-S/MIMO systems. Then a closed-form capacity expression is achieved in this paper. A special system, having two transmit-antennas and two/three receive-antennas, is taken for example to validate our theoretic analysis. Results show that the analytical results are very close to the simulations.

In this paper, by using the moment generating function (MGF) method, we derived the exact symbol error rate (SER) of multiple-input multiple-output (MIMO) system with transmit-antenna selection in a full-correlated fading channel. There are three assumptions in this work. The first is that the channel state information (CSI) is available only at the receiver but not at the transmitter. The second is that the transmit antenna selection is based on the instantaneous received signal power, and only one transmit antenna is chosen during a transmission period. The last is that the spatial correlation is present at both ends of the wireless communications link. A closed-form expression of SER is obtained. And a unified formula of SER for several popular digital linear modulations is presented. Several numerical examples are performed to validate our theoretical analysis.