A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the max operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.

Multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) aided cell-free massive multiple-input multiple-output (CF mMIMO) network is investigated. A long-term sum-rate maximization problem is formulated to jointly optimize the active beamforming at each access point (AP) and the passive beamforming at each STAR-RIS while satisfying the quality of service (QoS) requirements. To address this non-convex problem with challenges caused by user mobility and high-dimensional optimization variables, two deep reinforcement learning (DRL)-based beamforming algorithms are proposed. Firstly, a soft actor-critic (SAC)-based centralized joint beamforming algorithm is proposed, which adds maximum entropy term to the objective function and provides an exceptional exploration-exploitation trade-off. However, since the centralized scheme might suffer from high communication loads and latency, we generalize the proposed approach into a distributed control. A multi-agent SAC (MASAC)-based distributed beamforming algorithm is further proposed, which employs the centralized training and decentralized execution (CTDE) framework, where each AP plays a role of an agent and makes decisions based on local observations. Simulation results demonstrate that: 1) The multiple STAR-RISs can effectively improve the performance of the CF mMIMO networks compared to other baseline schemes; 2) Both SAC-based and MASAC-based beamforming algorithms can maximize the sum-rate and guarantee the QoS of users in the long term; and 3) The MASAC-based beamforming algorithm performs better and converges faster than the SAC-based beamforming algorithm as it reduces the overall complexity and alleviates pressure on the fronthaul link transmission by making decentralized decisions.

Wi-Fi-based gesture recognition faces significant performance degradation in cross-domain scenarios due to the distribution shifts between various environments, user locations, and facing orientations. To address this challenge, we propose WiSACL, a novel Wi-Fi-based gesture recognition framework that integrates innovative CSI signal processing with advanced subdomain adaptation techniques. We propose a novel Wi-Fi CSI phase difference representation that suppresses the environmental noise and highlights the motion features via phase ratio computation, wavelet denoising, and temporal differencing, which are then encoded into discriminative image representations for enhanced gesture recognition. Building upon this robust signal representation, we develop an Adaptive Distributioncalibrated Pseudo-Label (ADPL) module that progressively refines target labels through subdomain distribution alignment and confidence-aware selection. To fully exploit these pseudo-labels while mitigating the adverse effects of domain shift and label noise, we further introduce a contrastive learning (CL) model that explicitly enhances intra-class compactness and inter-class separation in the feature space. Extensive experiments on the Widar3.0 dataset demonstrate that WiSACL achieves an average accuracy of 98.62% across cross-location, cross-orientation, and cross-environment scenarios, significantly outperforming state-of-the-art methods and validating the effectiveness of our approach for robust cross-domain wireless gesture recognition.

As a representative backbone architecture of large artificial intelligence models, a Transformer-based architecture is proposed for learning beamforming in pinching antenna systems (PASS), which is calledPASSformer. A joint tri-beamforming optimization problem is developed, i.e., the digital, analog, and pinching beamforming, for fully realizing the benefits of mitigating long-distance path loss. The PASSformer incorporates a graph-based architecture with permutation properties, enabling it to adapt to different system sizes (e.g., varying numbers of users, waveguides, or pinching antennas) without re-training. To improve the learning performance, the PASSformer is with a staged architecture to learn the highly-coupled tri-beamforming. A training algorithm is also proposed to cope with the highly non-smooth loss function. Simulation results demonstrate the flexibility and generalizability of the PASSformer to different system settings and problem scales, enabling it to be deployed across diverse communication scenarios, including single-/multi-cell fully digital and hybrid transmit beamforming. The performance gain over conventional multi-input-multi-output systems brought by the reduced path loss positions is also validated.

A Pinching-Antenna SyStem (PASS)-assisted covert communication framework is proposed. PASS utilizes dielectric waveguides with freely positioned pinching antennas (PAs) to establish strong line-of-sight links. Capitalizing on the high reconfigurable flexibility of waveguides, the potential of PASS for covert communications is investigated. 1) For the single-waveguide single-PA (SWSP) scenario, a closed-form optimal PA position that maximizes the covert rate is first derived. Subsequently, a one-dimensional search is performed to obtain the optimal transmit power, while a truncation method is applied simultaneously to reduce the search region. With antenna mobility on a scale of meters, PASS can deal with the challenging situation of the eavesdropper enjoying better channel conditions than the legal user. 2) For the multi-waveguide multi-PA (MWMP) scenario, the positions of multiple PAs are optimized to enable effective pinching beamforming, thereby enhancing the covert rate. In particular, an upper bound on the malicious user’s beam gain over its position-uncertainty region is first derived. Building on this, the original optimization can be converted into an equivalent problem determined solely by the PA positions. To address this problem, a particle swarm optimization (PSO)–based method is devised to solve it efficiently. Numerical results demonstrate that: i) the proposed approaches can effectively resolve the optimization problems; ii) PASS achieves a higher covert rate than conventional fixed-position antenna architectures; and iii) with enhanced flexibility, the MWMP setup outperforms the SWSP counterpart.

A sensing-aided covert communication network empowered by pinching antenna systems (PASS) is proposed in this work. Unlike conventional fixed-position multiple-input multiple-output (MIMO) arrays, PASS reconfigures wireless channels by repositioning pinching antennas (PAs) to enhance transmission covertness. To further obtain the adversary’s channel state information (CSI), a sensing function is leveraged to track the malicious warden’s movements. In particular, this paper first proposes an extended Kalman filter (EKF) based approach to fulfilling the tracking function. Building on this, a covert communication problem is formulated as a joint design of beamforming, artificial noise (AN) signals, and PA positions. Then, the beamforming and AN design subproblems are resolved using a subspace approach, while the PA position optimization subproblem is handled by a deep reinforcement learning (DRL) approach by treating the evolution of the warden’s mobility status as a temporally correlated process. Numerical results are presented and demonstrate that: i) the EKF approach can accurately track the warden’s CSI with low complexity, ii) the effectiveness of the proposed solution is verified by its outperformance over the greedy and searching-based benchmarks, and iii) with new design degrees of freedom (DoFs), the performance of PASS is superior to the conventional fully-digital MIMO systems.

Beam alignment for multiple-input and multiple-output fluid antenna systems (MIMO-FAS) is studied, where two-sided beamforming and port activation are optimized without channel estimation to enhance transmission rate. In contrast to conventional position-fixed MIMO setups, MIMO-FAS leverages flexible beamforming to achieve higher gains with a smaller number of antennas. However, realizing these gains typically requires high-complexity channel estimation methods, especially in MIMO scenarios. To overcome this challenge, a channel estimation-free active-sensing framework for beam alignment in MIMO-FAS is proposed, which consists of three components: 1) A new ping-pong transmission protocol is conceived, enabling full-dimensional pilot reception through sequential sub-array activation. 2) Based on this protocol, two learning-based active-sensing algorithms are proposed for full-dimensional beam alignment via online and offline learning, respectively. 3) A greedy-policy-based method is developed to design the port activation matrices and associated beamforming vectors based on the active-sensing results. Numerical results demonstrate that: i) the proposed active-sensing framework can effectively utilize the advantages of FAS over conventional MIMO systems without channel estimations; and ii) the online-learning method enhances generalizability by eliminating the need for extensive centralized offline training, while the offline-learning method ensures robustness and low-complexity beam alignment by leveraging prior knowledge from the training phase.

Integrated satellite-terrestrial networks (ISTNs) provide crucial support for ubiquitous connectivity, whereas the inherent openness poses significant security threats. Covert communication that achieves intrinsic protection by concealing transmission behavior has become an ideal solution. This work studies a downlink covert satellite communication scenario under signal overlay. Existing research mostly carries out optimizations in the power domain. However, since waveforms serve as the physical carriers of information, their design critically impacts the covert communication performance via different adjustability to channel state information (CSI). Specifically, blindly enhancing demodulation reliability does not necessarily lead to better covert capacity and may even be counterproductive. OFDM is vulnerable to carrier frequency offset, while OTFS lacks the flexibility to accommodate CSI, resulting in difficulties applying existing candidate waveforms. In light of this, we propose a nimble waveform named orthogonal fractional dual index multiplexing (OFDIM) tolerant of fractional delay and Doppler. It enables common modulation schemes through integer indices and creates a fractional domain resilient to CSI. The closed-forms of the maximum covert transmit power, reliability, and spectral efficiency are derived. Fractional indices are optimized to improve the performance based on CSI. Simulations validate the theoretical analysis and demonstrate the benefits of the fractional domain in covert communication. Additionally, OFDIM incorporates a complementary security mechanism where the indices can act as dynamic ciphers and guarantee certain security even if the communication behavior is exposed.

A novel near-field integrated sensing and communications framework for secure unmanned aerial vehicle (UAV) networks with high time efficiency is proposed. A ground base station (GBS) with large aperture size communicates with one communication UAV (C-UAV) under the existence of one eavesdropping UAV (E-UAV), where the artificial noise (AN) is employed for both jamming and sensing purpose. Given that the E-UAV’s motion model is unknown at the GBS, we first propose a near-field localization and trajectory tracking scheme. Specifically, exploiting the variant Doppler shift observations over the spatial domain in the near field, the E-UAV’s three-dimensional (3D) velocities are estimated from echo signals. To provide the timely correction of location prediction errors, the extended Kalman filter (EKF) is adopted to fuse the predicted states and the measured ones. Subsequently, based on the real-time predicated location of the E-UAV, we further propose a joint GBS beamforming and C-UAV trajectory design scheme for maximizing the instantaneous secrecy rate, while guaranteeing the sensing accuracy constraint. To solve the resultant non-convex problem, an alternating optimization approach is developed, where the near-field GBS beamforming and the C-UAV trajectory design subproblems are iteratively solved by exploiting the successive convex approximation method. Finally, our numerical results unveil that: 1) the E-UAV’s 3D velocities and location can be accurately estimated in real time with our proposed framework by exploiting the near-field spherical wave propagation; and 2) the proposed framework achieves superior secrecy rate compared to benchmark schemes and closely approaches the performance when the E-UAV trajectory is perfectly known.

Few-shot semantic segmentation (FSS) often struggles with the intra-class diversity issue between query and support images, caused by the category-biased information provided by limited annotated support images for matching objects. While increasing the number of annotated support images could mitigate this bias, it is impractical within the few-shot learning framework. Therefore, our proposed Unlabeled Query Integration Few-Shot Segmentation (UQI-FSS) tackles this challenge by incorporating unlabeled query images into the learning paradigm. This approach aims to achieve a more comprehensive category representation, which is essential to enhance segmentation accuracy in various scenarios. However, integrating unlabeled query images directly requires careful management to prevent the dilution of vital information from the annotated support set. To address this issue, we present an Unlabeled Query Integration Network (UQINet), which adaptively extracts beneficial and suppresses detrimental information from the unlabeled query images. Specifically, we first introduce an Information Bridging Module to close the gap between support and unlabeled query features, generating a pseudo-support set enriched with additional category data. Next, we introduce a Query Fusion Module to incorporate query information from both prototype and pixel levels into the pseudo-support features, thus improving their adaptability to the query. Finally, we propose an Adaptive Selection Module to select effective category information and combine the pseudo-support features, thereby activating target objects for precise segmentation prediction. Experimental results show considerable performance improvements over previous methods on various FSS benchmarks. The application of this method to four challenging scenarios further underscores its versatility and practical value.

A dual-scale deployment (DSD) framework is proposed for pinching antenna systems (PASS), under which four implementation protocols are developed. In the proposed framework, coarse-scale deployment moves the pinching antenna (PA) over a wide range along the waveguide, while fine-scale deployment adjusts the PA with high precision within a local region. By jointly optimizing these two scales, the DSD framework fully exploits the flexibility of PA deployment while maintaining low computational complexity. Based on this framework, we establish a practical power-consumption model and derive closed-form expressions for the energy efficiency of PASS. An energy-efficiency maximization problem is then formulated to jointly optimize transmit precoding, PA radiation power, and dual-scale PA deployment. To solve this non-convex and highly coupled problem, a low-complexity penalty-based alternating optimization algorithm is proposed. Simulation results validate the accuracy of the theoretical analysis and the convergence of the proposed algorithm. The proposed DSD framework delivers about 70% higher energy efficiency than the conventional cell-free architecture and nearly atwofoldimprovement over MIMO systems.

Pinching antenna system (PASS) configures the positions of pinching antennas (PAs) along dielectric waveguides to change both large-scale fading and small-scale scattering, which is known as pinching beamforming. A novel non-orthogonal multiple access (NOMA) assisted PASS framework is proposed for downlink multi-user multiple-input multiple-output (MIMO) communications. The transmit power minimization problem is formulated to jointly optimize the transmit beamforming, pinching beamforming, and power allocation. To solve this highly nonconvex problem, both gradient-based and swarm-based optimization methods are developed. 1) For gradient-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed. The Lipschitz gradient surrogate function is constructed based on MM to tackle the nonconvex terms of this problem. Then, the joint optimization problem is decomposed into subproblems that are alternatively optimized based on PDD to obtain stationary closed-form solutions. 2) For swarm-based method, a fast-convergent particle swarm optimization and zero forcing (PSO-ZF) algorithm is proposed. Specifically, the PA position-seeking particles are constructed to explore high-quality pinching beamforming solutions. Moreover, ZF-based transmit beamforming is utilized by each particle for fast fitness function evaluation. Simulation results demonstrate that: i) The proposed NOMA assisted PASS and algorithms outperforms the conventional NOMA assisted massive antenna system. The proposed framework reduces over 95.22% transmit power compared to conventional massive MIMO-NOMA systems. ii) Swarm-based optimization outperforms gradient-based optimization by searching effective solution subspace to avoid stuck in undesirable local optima.

This paper considers communication between a base station (BS) to two users, each from one side of a simultaneously transmitting-reflecting reconfigurable intelligent surface (STAR-RIS) in the absence of a direct link. Rate-splitting multiple access (RSMA) strategy is employed and the STAR-RIS is subjected to phase errors. The users are equipped with a planar fluid antenna system (FAS) with position reconfigurability for spatial diversity. First, we derive the distribution of the equivalent channel gain at the FAS-equipped users, characterized by at-distribution. We then obtain analytical expressions for the outage probability (OP) and average capacity (AC), with the latter obtained via a heuristic approach. Our findings highlight the potential of FAS to mitigate phase imperfections in STAR-RIS-assisted communications, significantly enhancing system performance compared to traditional antenna systems (TAS) with only modest hardware complexity and negligible training or feedback overhead at the user side. Furthermore, we quantify the impact of practical phase errors on system efficiency, emphasizing the importance of robust and energy-efficient strategies for next-generation wireless networks.

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, three scenarios, multiple PAs for a single user (MPSU), a single PA for a single user (SPSU), and a single PA for multiple users (SPMU) are considered. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. As the further advance, closed-form expressions of approximated ergodic rate is derived when a single PA is fixed in the SPMU scenario. Our results demonstrate the following key insights: i) The proposed PASS significantly outperforms conventional Multiple-input Single-output networks by exploiting the flexibility of PAs; ii) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; iii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

With the increasing carrier frequency in next-generation wireless networks, conventional discrete aperture arrays (DAPA) are unable to fully meet the growing demands of sixth-generation wireless networks. To provide a higher degree of freedom, the continuous-aperture array (CAPA) technique emerges as a promising solution for next-generation networks. In this article, a CAPA-aided network is proposed to deliver access services to multiple users. The transmission power of a two-user pair in the downlink is optimized by using the Fourier transformation to derive tractable results. With the aid of Karush-Kuhn-Tucker (KKT) conditions, closed-form expressions for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are derived. Time division multiple access (TDMA) and spatial division multiple access (SDMA) are also considered as OMA schemes. Furthermore, DAPA is included as a benchmark for comparison. Our analytical and numerical results demonstrate the following: i) The proposed KKT-based approach achieves optimal performance in transmission power; ii) The minimal required transmission power for NOMA is lower than that for the OMA benchmark schemes; iii) A performance gap is observed between CAPA and DAPA in both NOMA and OMA, emphasizing the advantages of CAPA-aided networks.

The fundamental sensing limit of pinching-antenna systems (PASS) is studied from a Bayesian Cramér-Rao bound (BCRB) perspective. Compared to conventional CRB, the BCRB is independent of the exact values of sensing parameters and is not restricted by the unbiasedness of estimators, thus offering a global lower bound for evaluating sensing performance. A system where multiple targets transmit uplink pilots to a single-waveguide PASS under a time-division multiple access (TDMA) scheme is analyzed. In the single-target scenario, our analysis reveals a unique mismatch between the sensing centroid (i.e., the PA position that minimizes the BCRB) and the distribution centroid (i.e., the center of the target’s prior distribution), underscoring the necessity of pinching beamforming, i.e., repositioning PAs along the waveguide. In the multi-target scenario, two scheduling protocols are proposed: 1) pinch switching (PS), which performs separate pinching beamforming for each time slot, and 2) pinch multiplexing (PM), which applies a single pinching beamforming across all slots. Based on these protocols, both the total power minimization problem under a BCRB threshold and the min-max BCRB problem under a total power constraint are formulated. By leveraging Karush-Kuhn-Tucker (KKT) conditions, these problems are equivalently converted into a search over PA positions and solved using an element-wise algorithm. Numerical results show that: i) PASS, endowed with large-scale reconfigurability, can significantly enhance the sensing performance compared with conventional fixed-position arrays, and ii) PS provides more robust performance than PM at the cost of higher computational complexity.

o address the critical need for stable communication and energy efficiency in dynamic unmanned aerial vehicle (UAV) scenarios,o address the critical need for stable communication and energy efficiency in dynamic unmanned aerial vehicle (UAV) scenarios,T a pinching-antenna system (PASS)-enabled UAV delivery framework is proposed, which exploits the capability of PASS to establish a strong line-of-sight link and reduce the free-space pathloss. Aiming at achieving a balance between communication performance and energy efficiency, we define an effective utility function, construct a utility maximization problem, and develop an iterative joint optimization algorithm for pinching antenna (PA) activation vector and UAV delivery sequencing (IJO-PADS). More specifically, to solve the highly coupled mixed-integer nonlinear programming problem of PA activation vector optimization, we propose a pair of algorithms: 1) Branch-and-Bound (BnB) algorithm for finding global optimum; 2) incremental search and local refinement (ISLR) algorithm for reducing computational complexity. With the optimized PA activation vector, we define the path weight between a pair of nodes, which accounts for communication rate reward and energy consumption penalty. To maximize sum pate weight, we propose a genetic algorithm and dynamic programming (GA-DP) hybrid optimization method to tackle the NP-hard problem of delivery sequence planning, where a GA performs global exploration to generate candidate solutions, while a DP performs local refinement to obtain elite solutions. Simulation results indicate that: i) the proposed IJO-PADS framework converges within a moderate number of iterations; ii) the proposed algorithms (BnB, ISLR, GA-DP) outperform several benchmarks, demonstrating the effectiveness of our designs for PA activation and delivery sequence planning; iii) PASS is superior to conventional MIMO systems, due to PASS’s flexible PA activation and low-attenuation waveguide transmission.

A segmented waveguide-enabled pinching-antenna system (SWAN) is proposed, in which a segmented waveguide composed of multiple short dielectric waveguide segments is employed to radiate or receive signals through the pinching antennas (PAs) deployed on each segment. Based on this architecture, three practical operating protocols are proposed: segment selection (SS), segment aggregation (SA), and segment multiplexing (SM). For uplink SWAN communications, where one PA is activated per segment, the segmented structure eliminates the inter-antenna radiation effect, i.e., signals captured by one PA may re-radiate through other PAs along the same waveguide. This yields a tractable and physically consistent uplink signal model for a multi-PA pinching-antenna system (PASS), which has not been established for conventional PASS using a single long waveguide. Building on this model, PA placement algorithms are proposed to maximize the uplink signal-to-noise ratio (SNR). Closed-form expressions for the received SNR under the three protocols are derived, and the corresponding scaling laws with respect to the number of segments are analyzed. It is proven that the segmented architecture reduces both the average PA-to-user distance and the PA-to-feed distance, thereby mitigating both large-scale path loss and in-waveguide propagation loss. These results are extended to downlink SWAN communications, where multiple PAs are activated per segment, and PA placement methods are proposed to maximize the downlink received SNR under the three protocols. Numerical results demonstrate that: i) among the three protocols, SM achieves the best performance, followed by SA and then SS; and ii) for all protocols, the proposed SWAN achieves a higher SNR than conventional PASS with a single long waveguide in both uplink and downlink scenarios.

The Pinching-Antenna SyStem (PASS) reconstructs wireless channels throughpinching beamforming, wherein the activated positions of pinching antennas along dielectric waveguides are optimized to shape the radiation pattern. The aim of this article is to analyze the performance limits of employing PASS in integrated sensing and communications (ISAC). Specifically, a PASS-assisted ISAC system is considered, where a pinched waveguide is utilized to simultaneously communicate with a user and sense a target. Closed-form expressions for the achievable communication rate (CR) and sensing rate (SR) are derived to characterize the information-theoretic limits of this dual-functional operation. i) For the single-pinch case, closed-form solutions for the optimal pinching antenna location are derived undersensing-centric (S-C),communications-centric (C-C), andPareto-optimaldesigns. On this basis, the CR-SR trade-off is characterized by deriving the full CR-SR rate region, which is shown to encompass that of conventional fixed-antenna systems. ii) For the multiple-pinch case, an antenna location refinement method is applied to obtain the optimal C-C and S-C pinching beamformers. As a further advance, inner and outer bounds on the achievable CR-SR region are derived using an element-wise alternating optimization technique and by invoking Cauchy-Schwarz and Karamata’s inequalities, respectively. Numerical results demonstrate that: i) the derived bounds closely approximate the true CR-SR region; and ii) PASS can achieve a significantly larger rate region than conventional-antenna systems.

The pinching-antenna system (PASS) reconfigures wireless links through pinching beamforming, in which the activated locations of pinching antennas (PAs) along dielectric waveguides are optimized. This article investigates the application of PASS in multicast communication systems, where pinching beamforming is designed to maximize the multicast rate. i) In the single-waveguide scenario, a closed-form solution for the optimal activated location is derived under the assumption of a single PA and linearly distributed users. Based on this, a closed-form expression for the achievable multicast rate is obtained and proven to be larger than that of conventional fixed-location antenna systems. For the general multiple-PA case with arbitrary user distributions, an element-wise alternating optimization (AO) algorithm is proposed to design the pinching beamformer. ii) In the multiple-waveguide scenario, an AO-based method is developed to jointly optimize the transmit and pinching beamformers. Specifically, the transmit beamformer is updated using a majorization-minimization (MM) framework together with second-order cone programming (SOCP), while the pinching beamformer is optimized via element-wise sequential refinement. Numerical results are provided to demonstrate that: i) PASS achieves significantly higher multicast rates than conventional fixed-location antenna systems, particularly when the number of users and spatial coverage increase; ii) increasing the number of PAs further improves the multicast performance of PASS.

This article addresses the design of multigroup multicast communications in the pinching-antenna system (PASS). A PASS-enabled multigroup transmission framework is proposed to maximize multicast rates under a couple of transmission architectures: waveguide-division (WD) and waveguide-multiplexing (WM). 1) For WD, an element-wise sequential optimization strategy is proposed forpinching beamforming, i.e., optimizing the activated positions of pinching antennas along dielectric waveguides. Meanwhile, a log-sum-exp projected gradient descent algorithm is proposed for transmit power allocation across waveguides. 2) For WM, a majorization-minimization (MM)-based framework is proposed to tackle the problem’s non-smoothness and non-convexity. On this basis, a low-complexity element-wise sequential optimization method is developed for pinching beamforming using the MM surrogate objective. Furthermore, the optimal transmit beamformer structure is derived from the MM surrogate objective using the Lagrange duality, with an efficient transmit beamforming algorithm proposed using projected adaptive gradient descent. Numerical results demonstrate that: i) both WD and WM architectures in PASS achieve significant multicast rate improvements over conventional MIMO techniques, especially for systems with large service areas; ii) WM is more robust than WD in dense deployments, while WD excels when user groups are spatially separated.

This article investigates secure multicast communications in pinching-antenna systems (PASS), where pinching beamforming is enabled by adaptively adjusting pinching antenna (PAs) positions along waveguides to improve multicast security. Specifically, a PASS-based secure multicast framework is proposed, in which joint optimization of transmit and pinching beamforming is conducted to maximize the secrecy multicast rate. i) For the single-group multicast scenario, an alternating optimization (AO) framework is employed, where the pinching beamformer is updated via an element-wise sequential optimization method. The transmit beamformer is designed via a semidefinite relaxation (SDR) formulation for an upper-bound solution, while a Dinkelbach-alternating direction method of multipliers (ADMM) offers a low-complexity alternative. ii) For the multi-group multicast scenario, transmit and pinching beamformers are alternately optimized under a majorization-minimization (MM) framework. The transmit beamformer is obtained via SDR or an efficient second-order cone programming (SOCP) method, while the pinching beamformer is updated through MM-based element-wise sequential update strategy. Numerical results are provided to demonstrate that: (i) PASS consistently outperform conventional fixed-location antenna architectures in terms of secrecy performance across various configurations; and (ii) the performance advantage of PASS over fixed-location architectures becomes more significant with increased service region, larger antenna arrays, and higher user and eavesdropper densities.

Low earth orbit (LEO) satellite constellations are becoming a cornerstone of next-generation satellite networks, enabling worldwide high-precision navigation and high-quality remote sensing. This paper proposes a novel dual-function LEO satellite constellation frame structure that effectively integrating navigation and remote sensing. Then, the Cramer-Rao bound (CRB)-based positioning, velocity measurement, and timing (PVT) error and the signal-to-ambiguity-interference-noise ratio (SAINR) are derived as performance metrics for navigation and remote sensing, respectively. Based on it, a joint beamforming design is proposed by minimizing the average weighted PVT error for navigation user equipments (UEs) while ensuring SAINR requirement for remote sensing. Simulation results validate the proposed multi-satellite cooperative beamforming design, demonstrating its effectiveness as an integrated solution for next-generation multi-function LEO satellite constellations.

The sixth generation mobile communication system promises to achieve ubiquitous coverage. Although integrated satellite-terrestrial networks (ISTNs) offer a direct resolvent, high mobility and openness pose challenges to reliability. Orthogonal time frequency space (OTFS) mitigates the limitation of orthogonal frequency division multiplexing (OFDM) in combating time-selective fading with the cost of additional complexity. Nonetheless, the diverse scenarios lead to differentiation in requirements from the perspective of waveform design. Therefore, a single waveform is inadequate to be uniformly adopted in ISTNs, further aggravated by the difficulty in upgrading satellite hardware. Consequently, there is an urgent need for a soft approach capable of switching between traditional waveforms while maintaining reliable communication under adverse conditions. A fractional dual index transform is introduced, enabling seamless switching and fusion across time, frequency, delay, and Doppler domains through two tunable indices. Based on this, fractional dual index division multiplexing (FDIDM) is designed along with input-output relationships and feasible decoders. FDIDM generalizes typical waveforms like OFDM and OTFS as special cases with the same complexity and improves performance through indices optimization at the cost of additional computational overhead. Theoretical analysis is conducted to obtain the closed-form symbol error rate (SER) and performance boundaries of FDIDM. Simulation results validate the deduction and demonstrate the advantages of cross-domain modulation over existing waveforms.

The fundamental capacity limits of pinching-antenna systems (PASS)-based multi-user communication network are investigated. Two practical pinching-antenna configuration strategies are considered, namelymultiple-time discrete activationandone-off continuous sliding. For each strategy, the capacity and rate regions are characterized under the non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, respectively. 1) For NOMA, the capacity region achieved by the discrete activation is first characterized. In particular, the ideal case with the infinite number of activation times is considered, where the optimal PA activation and resource allocation scheme is derived. It is shown that different user groups or decoding orders are served via time-sharing. Inspired by this result, an inner bound of capacity region is obtained for the practical case with a finite number of activation times. Then, for the continuous sliding case, the inner bound of capacity region is characterized by alternately optimizing the resource allocation and PAs’ continuous positions. 2) For OMA, the rate region is first obtained for the discrete activation case. It is unveiled that multiple users are successively served. Then, by alternately optimizing the resource allocation and PAs’ continuous positions, the inner bound of rate region is obtained for the continuous sliding case. Numerical results demonstrate that i) PASS can significantly improve the capacity performance compared with the conventional fixed-antenna systems; ii) the capacity gain can be further enhanced by using the proposed PA activation and sliding and resource allocation schemes; and iii) the continuous sliding has the potential to outperform the discrete activation in NOMA, whereas the latter performs better in OMA.

Pinching-antenna systems (PASS) improve wireless links by configuring the locations of activated pinching antennas along dielectric waveguides, namely pinching beamforming. In this paper, a novel adjustable power radiation model is proposed for PASS, where power radiation ratios of pinching antennas can be flexibly controlled by tuning coupling spacing between pinching antennas and waveguides. The closed-form coupling spacings are derived to achieve flexible and equal-power radiation. Based on the commonly-assumed equal-power radiation, a practical PASS framework relying on discrete activation is considered, where pinching antennas can only be activated among a set of predefined locations. A transmit power minimization problem is formulated, which jointly optimizes the transmit beamforming, pinching beamforming, and the numbers of activated pinching antennas, subject to each user’s minimum rate requirement. (1) To obtain globally optimal solutions of the resulting highly coupled mixed-integer nonlinear programming (MINLP) problem, branch-and-bound (BnB)-based algorithms are proposed for both single-user and multi-user scenarios. (2) A low-complexity many-to-many matching algorithm is further developed. Combined with the Karush-Kuhn-Tucker (KKT) theory, locally optimal and pairwise-stable solutions are obtained within polynomial-time complexity. Simulation results demonstrate that: (i) PASS significantly outperforms conventional multi-antenna architectures, particularly when the number of users and the spatial range increase; and (ii) The proposed matching-based algorithm achieves near-optimal performance, resulting in only a slight performance loss while significantly reducing computational overheads. Code is available at https://github.com/xiaoxiaxusummer/PASS_Discrete.

Pinching-antenna systems (PASS) have emerged as a promising technology due to their ability to dynamically reconfigure wireless propagation environments. A novel PASS-based multi-user non-orthogonal multiple access (NOMA) framework is proposed by exploiting the waveguide-division (WD) transmission characteristic. Specifically, each NOMA user cluster is served by one dedicated waveguide, and the corresponding pinching beamforming is exploited to enhance the intra-cluster performance while mitigating the inter-cluster interference. Based on this framework, a sum-rate maximization problem is formulated for jointly optimizing power allocation, pinching beamforming, and user scheduling. To solve this problem, a two-step algorithm is developed, which decomposes the original problem into two subproblems. For the joint power allocation and pinching beamforming design, a penalty dual decomposition (PDD) algorithm is proposed to obtain the locally optimal solutions. Specifically, the coupling constraints are alleviated through augmented Lagrangian relaxation, and the resulting augmented Lagrangian (AL) problem is decomposed into four subproblems, which are solved by the block coordinate descent (BCD) method. For the user scheduling, a low-complexity matching algorithm is developed to solve the user-to-waveguide assignment problem. Simulation results demonstrate that 1) the proposed PASS-based NOMA framework under the WD transmission structure achieves significant sum-rate gain over conventional fixed-position antenna systems and orthogonal multiple access (OMA) scheme; and 2) the proposed matching-based user scheduling algorithm achieves near-optimal user-waveguide association with low computational complexity.

A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communication (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS employs movable elements with adjustable positions, offering additional spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and symbol estimation error. An algorithm based on alternating minimization is devised to handle the resultant non-convex problem, where the subproblems are solved via augmented Lagrangian method, quadratic programming, semidefinite relaxation, and majorization-minimization. A key challenge is that the element positions affect both incident and reflective channels, leading to the high-order composite objective functions. As a remedy, the high-order terms are transformed into linear and linear-difference forms by exploiting the structural characteristics of fRIS and the channels. Numerical results demonstrate the superiority of the proposed scheme over conventional RIS-aided ISAC and other benchmarks.

A novel near-field wideband integrated sensing and communication framework is proposed to address the prohibitively high pilot overhead challenge in extremely large-scale MIMO systems. Unlike conventional approaches that rely on exhaustive two-dimensional codebook search, a unified architecture leveraging true-time-delay-based rainbow beamforming with controllable distance-dependent beam squint is proposed to extend spatial coverage. Furthermore, the inter-antenna phase ambiguity is harnessed to introduce beam split phenomena, enabling simultaneous multi-angle and multi-distance sensing within a single pilot transmission. Based on this architecture, a two-stage low-complexity sensing protocol is carried out, where distance-ring identification via beam-split-enhanced rainbow beams is performed in the first stage using sub-array structures, followed by angle refinement in the second stage. To mitigate frequency-dependent beamwidth variations, a frequency-compensated joint reconstruction algorithm based on virtual grid mapping and sparse optimization is proposed. Additionally, an echo-aided velocity estimation method exploiting intra-symbol Doppler diversity across subcarriers is developed, eliminating the need for multiple pulse transmissions. Simulation results demonstrate that: 1) complete spatial coverage is achieved with only two OFDM symbols, representing over 98% overhead reduction compared to exhaustive search methods; 2) the proposed scheme achieves superior localization accuracy with root-mean-square errors below 0.001 in normalized angle domain and 0.01 in distance-ring domain at moderate SNR; 3) communication rates are improved by 7% to 15% compared to conventional near-field beam training approaches under identical pilot budgets.

To meet the emerging demands for rapid data aggregation and reliable information transmission in future wireless applications, a novel system architecture integrating Over-the-Air Computation (AirComp) and downlink multi-user communication via a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed in this paper. In the considered cellular scenario, an unmanned aerial vehicle (UAV) carries a STAR-RIS beneath its fuselage, creating a programmable aerial platform that concurrently serves Internet-of-Things (IoT) devices and conventional mobile users. The STAR-RIS operates in transmission mode to enable efficient wireless data aggregation of IoT devices, while its reflection mode establishes high-quality downlink channels from the base station (BS) to multiple users. Capturing the true electromagnetic behavior of the STAR-RIS, we explicitly model the practical coupling between the reflection and transmission phase shifts. Two optimization problems are then formulated: one minimizes AirComp distortion and the other maximizes the minimum user rate in the downlink. Both non-convex problems are tackled by efficient iterative algorithms derived from the penalty dual decomposition (PDD) framework. Extensive simulations confirm that the proposed design markedly outperforms baseline approaches and its performance can approach that of ideal phase-shift control by enhancing the key system parameters. Additionally, the trade-off between computation and communication performance is demonstrated.

Low Earth orbit satellite (LEOS) communications are vital for advancing global connectivity. However, these systems are vulnerable to threats from malicious jamming and the conflict between safety and transmission rates. Covert communication is a promising technology that can reduce the detectability of wireless transmissions at high rates. Therefore, in this paper, we introduced atwo-layer holographicapproach in the LEOS covert communication system based on holographic jamming-parasitic modulation (HJPM) and holographic multiple-input multiple-output (HMIMO). In HJPM, the transmitter superim-poses information signals onto jamming signals, allowing the legitimate receiver to reconstruct useful information from the jamming, effectively hiding the information within the jamming. The HMIMO surface utilizes holographic beamforming to achieve dynamic high-directional gains, thereby enhancing communication for legitimate users while meeting covertness constraints. Additionally, we proposed an optimization algorithm, i.e., Tri-CoHo, that combines elastic parasitic modulation depth with hybrid beamforming to maximize covert transmission rate in LEOS communications. Results showed that our scheme achieves a higher communication performance while maintaining a high level of undetectability compared to benchmarks.

Pinching-antenna systems (PASS) represent an innovative advancement in flexible-antenna technologies, aimed at significantly improving wireless communications by ensuring reliable line-of-sight connections and dynamic antenna array reconfigurations. To employ multi-waveguide PASS in multi-user communications, three practical transmission structures are proposed, namely waveguide multiplexing (WM), waveguide division (WD), and waveguide switching (WS). Based on the proposed structures, the joint baseband signal processing and pinching beamforming design is studied for a general multi-group multicast communication system, with the unicast communication encompassed as a special case. A max-min fairness (MMF) problem is formulated for each proposed transmission structure, subject to the maximum transmit power constraint. For WM, to solve the highly-coupled and non-convex MMF problem with complex exponential and fractional expressions, a penalty dual decomposition (PDD)-based algorithm is invoked for obtaining locally optimal solutions. Specifically, the augmented Lagrangian relaxation is first applied to alleviate the stringent coupling constraints, which is followed by the block decomposition over the resulting augmented Lagrangian function. Then, the proposed PDD-based algorithm is extended to solve the MMF problem for both WD and WS. Furthermore, a low-complexity algorithm is proposed for the unicast case employing the WS structure, by simultaneously aligning the signal phases and minimizing the large-scale path loss at each user. Finally, numerical results reveal that: 1) the MMF performance is significantly improved by employing the PASS compared to conventional fixed-position antenna systems; 2) WS and WM are suitable for unicast and multicast communications, respectively; 3) the performance gap between WD and WM can be significantly alleviated when the users are geographically isolated.

The joint transmit and pinching beamforming design for spectral efficiency (SE) and energy efficiency (EE) tradeoff in pinching-antenna systems (PASS) is proposed, under practical channel and energy consumption models. In the single-user scenario, it is proved that the optimal pinching antenna (PA) positions are independent of the transmit beamforming. Based on this insight, a two-stage joint beamforming design is proposed. Specifically, in the first stage, a general PA placement framework is proposed for multi-waveguide systems. In the second stage, the closed-form solution for the optimal transmit beamformer is derived given the optimized PA positions. In the multi-user scenario, an alternating optimization (AO)-based joint beamforming design is proposed to balance the SE-EE performance while taking the quality-of-service (QoS) requirements into account. It is proved that the proposed AO-based algorithm is guaranteed to converge when no constraints are violated in PA placement subproblem. Numerical results demonstrate that: 1) the proposed algorithms effectively improve joint SE-EE performance; 2) PASS exhibits strong robustness against variations in the service area along the waveguide direction.

A novel pinching-antenna systems (PASS)-enabled secure wireless communication framework is proposed. By dynamically adjusting the positions of dielectric particles, namely pinching antennas (PAs), along the waveguides, PASS introduces a novel concept of pinching beamforming to enhance the performance of physical layer security. A fundamental PASS-enabled secure communication system is considered with one legitimate user and one eavesdropper. Both single-waveguide and multiple-waveguide scenarios are studied. 1) For the single-waveguide scenario, the secrecy rate (SR) maximization is formulated to optimize the pinching beamforming. A PA-wise successive tuning (PAST) algorithm is proposed, which ensures constructive signal superposition at the legitimate user while inducing a destructive legitimate signal at the eavesdropper. 2) For the multiple-waveguide scenario, artificial noise (AN) is employed to further improve secrecy performance. A pair of practical transmission architectures are developed:waveguide division (WD)andwaveguide multiplexing (WM). The key difference lies in whether each waveguide carries a single type of signal or a mixture of signals with baseband beamforming. For the SR maximization problem under the WD case, a two-stage algorithm is developed, where the pinching beamforming is designed with the PAST algorithm and the baseband power allocation among AN and legitimate signals is solved using successive convex approximation (SCA). For the WM case, an alternating optimization algorithm is developed, where the baseband beamforming is optimized with SCA and the pinching beamforming is designed employing particle swarm optimization. Numerical results demonstrate that i) PASS can significantly improve the secrecy performance over conventional antenna systems in both scenarios; ii) the proposed PAST algorithm for the single-waveguide scenario is efficient, especially when the number of PAs is even or large; iii) WM provides higher and more stable performance at the cost of increased complexity, while WD serves as a simple yet scalable alternative, which is effective when a large number of PAs are deployed.

Emerging multimodal systems present new requirements for mobile edge networks to handle multimodal data. In this paper, a novel multimodal mobile edge computing (MEC) framework is proposed, which synchronizes the multimodal data acquisition, communication, and computation to ensure both consistency and efficiency. The key objective is to simultaneously maximize multimodal data throughput and minimize the energy consumption of mobile terminals (MTs) under synchronization and resource constraints. A multi-objective optimization (MOP) is formulated, where the sensor activation time, computation offloading, and resource allocation are jointly optimized. To solve this nonconvex problem, a dual-layer Lagrangian multiplier method (D-LMM) is developed. It decouples the optimization into an upper-level throughput maximization and a lower-level energy minimization. The former is converted into a convex problem via quadratic transformation, yielding a stationary solution for sensor activation times, while the latter is solved by alternating optimization. The D-LMM algorithm is proven to converge to a local optimum. Simulation results verify that the proposed framework significantly improves throughput and reduces MT energy consumption. The synchronization-aware multimodal coordination further ensures sufficient data collection and robust performance across varying network scales and resource conditions, enabling reliable downstream operations.

Mobile Edge Generation (MEG) is presented as a distributed framework in which an identical diffusion model (DM) is deployed on both an edge server (ES) and user equipment (UE). In MEG, most computations and generation steps of UEs are offloaded to the ES. However, heterogeneous user preferences cannot be captured by a uniform DM. To address this, a Personalized Mobile Edge Generation (P-MEG) framework is proposed, where a lightweight personalized U Net is trained on the UE in collaboration with the pre-trained DM from the ES. During inference, pre-trained ES features are fused with UE features through scaling coefficients that encode user-specific preferences. The training stability of P MEG and the robustness of feature fusion under noisy wireless channels are theoretically investigated, where bounds are derived on forward and backward feature oscillations, backpropagation gradients, and feature fusion errors in the presence of additive white Gaussian noise (AWGN) noise. These bounds are shown to depend on the fusion scale, and robustness under AWGN follows the same dependence. A multi-U-Net training model with AWGN perturbations is introduced to emulate over-the air training. Inspired by these insights, a constant scaling connection (CSC) method is proposed to stabilize training by exponentially scaling the fusion coefficients, and a random mask training (RMT) strategy is introduced to reduce computational requirements by adjusting transmission ratios of personalized features. Experimental evaluations on MNIST, EMNIST and PACS demonstrate that: 1) P-MEG enables effective personalized image generation, 2) RMT alleviates computational demands with only slight training overhead, and 3) CSC stabilizes feature oscillations under noisy channels, yielding a 1.4-fold acceleration in training.

A novel non-orthogonal multiple access (NOMA) based low-delay service framework is proposed for fog radio access networks (F-RANs). Fog access points (FAPs) leverage NOMA for local delivery of cached content, while the cloud access point employs NOMA to simultaneously push content to FAPs and directly serve users. Based on this model, a delay minimization problem is formulated by jointly optimizing user association, cache placement, and power allocation. To address this non-convex mixed-integer nonlinear programming problem, an alternating optimization (AO) algorithm is developed, which decomposes the original problem into two subproblems, namely joint user association and cache placement, and power allocation. In particular, a low-complexity algorithm is designed to optimizing the user association and cache placement strategy using the McCormick envelope theory and Lagrangian partial relaxation. The power allocation is optimized by invoking the successive convex approximation. Simulation results reveal that: 1) the proposed AO-based algorithm effectively balances between the achieved performance and computational efficiency, and 2) the proposed NOMA-based F-RANs framework significantly outperforms orthogonal multiple access-based F-RANs systems in terms of average transmission delay in different scenarios.

A deep reinforcement learning (DRL) method is proposed for hybrid beamforming in near-field wideband communication systems, aiming to alleviate the harmful near-field beam splitting issue caused by the spatial-wideband effect. Compared to the far-field beam splitting issue that occurs only in the angle domain, the near-field beam splitting issue extends to the distance domain and is thus more challenging to address. A hybrid beamforming architecture with true-time delayers (TTDs) is exploited to address the near-field beam splitting issue and facilitate the maximization of spectral efficiency (SE) across large bandwidth for downlink multi-user communications. However, the conventional iterative hybrid beamforming optimization methods, such as the weighted minimum mean-square method, often exhibit high computational complexity and are thus difficult to adapt the dynamic channel in a real-time manner. Hence, a DRL-based algorithm is propose to select fully-digital codewords from the designed near-field wideband beamforming codebook. To further approximate the selected codewords in the considered hybrid-beamforming architecture, a three-stage hybrid beamforming approximation (HBA) algorithm is proposed to minimize the fully-digital approximation error by jointly optimizing the TTD-based analog beamformers and baseband digital beamformers. Numerical results show that: (1) the proposed DRL-HBA solution for hybrid beamforming significantly increases the SE; (2) the hybrid beamforming architecture with TTDs can eliminate the near-field beam splitting effect and achieve beam focusing across wide band.

This paper investigates a secure aerial reconfigurable intelligent surface (A-RIS) communication system, where user mobility, imperfect channel state information (CSI), and RIS phase errors induced by unmanned aerial vehicle (UAV) jitter significantly degrade performance. To address these challenges, we formulate a joint optimization problem for UAV trajectory, base station (BS) we propose abeamforming, and A-RIS beamforming to maximize the minimum secrecy energy efficiency (SEE), subject to constraints on user secrecy rates and UAV energy efficiency. To solve this highly non-convex problem, we propose a novel reinforcement learning framework termed IA-CSORL based on the twin-twin-delayed deep deterministic policy gradient (TTD3) architecture, which incorporates two novel modules. Specifically, we develop the phase-aware relativistic adaptive descent (PRAD) algorithm is proposed, which embeds the learning process into a conformal Hamiltonian system. By integrating gradient-based phase error correction and adaptive momentum adjustment, PRAD effectively counteracts phase noise and stabilizes training. Furthermore, we design an environment-state interactive attention (ESIA) mechanism to dynamically fuse UAV positioning and environmental features, enhancing state representation and deployment accuracy. Numerical results demonstrate that IA-CSORL significantly outperforms existing RL baselines in terms of both robustness and convergence performance. Moreover, IA-CSORL achieves superior beamforming accuracy under phase errors and CSI imperfections and provides a better trade-off between sum secrecy rate (SSR) and SEE, with performance gains becoming more significant as the number of RIS elements increases.

The popularity of artificial intelligence generated content (AIGC) is prompting the deployment of large language model (LLM) from cloud to edge networks, leading to mobile edge generation (MEG). Due to high latency and limited computational capabilities of mobile devices, personalized image generation for mobile healthcare and education requires edge-mobile generation paradigm. In this paper, a novel non-orthogonal multiple access (NOMA) assisted multi-user MEG framework is proposed for text-guided mobile image generation. NOMA enables concurrent access from multiple user equipments (UEs) to the edge-deployed large model, facilitating adjustable generation splitting. Specifically, the edge server (ES) partially generates the image and transmits it via downlink NOMA, while UEs complete the remaining parts using lightweight models. Both unlimited and limited energy budget scenarios are considered. 1) For unlimited energy budget, a joint generation splitting ratio and NOMA power allocation optimization problem is formulated, which minimizes the maximum (min-max) latency of UEs to ensure fairness. The closed-form globally optimal solutions based on Karush-Kuhn-Tucker (KKT) and Lambert-W theory are derived. Moreover, the superiority of MEG-NOMA over conventional MEG-orthogonal multiple access (OMA) is mathematically proved. 2) For limited energy budget, a multi-objective programming problem is formulated to minimize the latency of each UE, which leads to a user-centric latency minimization problem. The closed-form solutions of generation splitting ratio and power allocation are derived. Simulation results illustrate that the proposed MEG-NOMA outperforms the MEG-OMA in both two-user and multi-user cases. Compared to conventional MEG-OMA, the MEG-NOMA framework reduces the min-max latency and the user-centric latency by 33.01% and 9.86%, respectively.

Due to the increasing number of users and antennas in extremely large antenna arrays (ELAA) based integrated sensing and communication (ISAC) systems, the complexity of beamforming optimization becomes overwhelming, which impedes real-time and cost-efficient ISAC deployment in practice. Specifically, a general ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning algorithms to provide a low complexity and transferable (LCT) solution for ISAC beamforming. The proposed LCT beamforming optimization framework includes three modules: 1) an unsupervised learning based feature extraction algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL agent. Simulation results demonstrate that the proposed LCT framework outperforms the baseline RL algorithm by optimizing the intuitional beampattern rather than beamforming. Moreover, the LCT framework provides a solution for low-cost beamforming optimization in ISAC systems. The trained RL module can be transferred without retraining when the antenna or user number changes.

A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems. The beamforming optimization problem is first formulated, and it is mathematically proved that the optimal beamforming lies in the subspace spanned by users’ conjugate channel responses. Two challenges are encountered when directly applying deep neural networks (DNNs) for solving the formulated problem, i) both the input and output spaces are infinite-dimensional, which are not compatible with DNNs. The finite-dimensional representations of inputs and outputs are derived to address this challenge. ii) A closed-form loss function is unavailable for training the DNN. To tackle this challenge, two additional DNNs are trained to approximate the operations without closed-form expressions for expediting gradient back-propagation. To improve learning performance and reduce training complexity, the permutation equivariance properties of the mappings to be learned are proved. As a further advance, the DNNs are designed as graph neural networks to leverage the properties. Numerical results demonstrate that: i) the proposed DeepCAPA framework achieves higher spectral efficiency and lower inference complexity compared to existing numerical algorithms, ii) DeepCAPA approaches the performance upper bound of optimizing beamforming in the spatially discrete array systems as the number of antennas in a fixed-sized area tends toward infinity, and iii) DeepCAPA can be well-generalized to different system settings.

The beamfocusing property can be effectively utilized to enhance system performance in near-field communications. In this paper, the secure transmission for near-field extremely large-scale MIMO (XL-MIMO) communication systems is investigated, where the base station (BS) transmits private confidential information to legitimate users under the threat of a potential eavesdropper. To explore the unique characteristics of near-field physical layer security (PLS), a specific scenario involving a single legitimate user and a single eavesdropper is first considered. It is rigorously proved that 1) Artificial noise (AN) plays an essential role in near-field secure communications, facilitating the transformation of insecure systems into secure ones, and 2) allocating even a small portion of power for AN substantially improves security, compared with scenarios where AN is not employed. Additionally, for the general scenario with multiple legitimate users, a conventional scheme is initially introduced by employing semi-definite relaxation and successive convex approximation algorithms. Subsequently, an efficient low-complexity beamforming scheme incorporating AN is proposed to ensure secure transmission in the near-field region. Furthermore, the closed-form solution for optimal beamforming power allocation is derived. Extensive numerical results show that 1) the utilization of near-field beamfocusing exhibits significant performance gains in enhancing PLS in comparison with far-field communications, and 2) the proposed scheme achieves performance comparable to the high computational complexity conventional scheme, while significantly reducing computational complexity.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. Unlike traditional fixed-position multiple-input multiple-output (MIMO) systems, a FAS possesses position reconfigurability to switch on-demand amongNpredefined ports over a prescribed space. This paper explores the performance of a single-input single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closed-form bounds for the outage probability of the Rx-SISO-FAS model, as well as exact and closed-form lower bounds for the ergodic rate. Importantly, we also analyze the performance considering both uniform linear array (ULA) and uniform planar array (UPA) configurations for the ports of the FAS. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,N, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:i) the Rx-SISO-FAS model can enhance performance under spatially-correlated Rician fading channels over the fixed-position antenna counterpart;ii) the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime;iii) FAS can outperform anLbranches maximum ratio combining (MRC) system under Rician fading channels; andiv) when the number of ports is identical, UPA outperforms ULA.

A Cramér-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed. In contrast to conventional spatially discrete arrays (SPDAs), CAPAs emit electromagnetic (EM) probing signals through continuous source currents for target sensing, thereby exploiting the full spatial degrees of freedom (DoFs). The maximum likelihood estimation (MLE) method for estimating target locations in the near-field region is developed. To evaluate the NISE performance with CAPAs, the CRB for estimating target locations is derived based on continuous transmit and receive array responses of CAPAs. Subsequently, a CRB minimization problem is formulated to optimize the continuous source current of CAPAs. This results in a non-convex, integral-based functional optimization problem. To address this challenge, the optimal structure of the source current is derived and proven to be spanned by a series of basis functions determined by the system geometry. To solve the CRB minimization problem, a low-complexity subspace manifold gradient descent (SMGD) method is proposed, leveraging the derived optimal structure of the source current. Our simulation results validate the effectiveness of the proposed SMGD method and further demonstrate that i) the proposed SMGD method can effectively solve the CRB minimization problem with reduced computational complexity, and ii) CAPA achieves a tenfold improvement in sensing performance compared to its SPDA counterpart, due to full exploitation of spatial DoFs.

Accurate channel state information (CSI) acquisition for massive multiple-input multiple-output (MIMO) systems is essential for future mobile communication networks. Channel fingerprint (CF), also referred to as channel knowledge map, is a key enabler for intelligent environment-aware communication and can facilitate CSI acquisition. However, due to the cost limitations of practical sensing nodes and test vehicles, the resulting CF is typically coarse-grained, making it insufficient for wireless transceiver design. In this work, we introduce the concept of CF twins and design aconditionalgenerative diffusion model (CGDM) with strong implicit prior learning capabilities as the computational core of the CF twin to establish the connection between coarse- and fine-grained CFs. Specifically, we employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CFconditionedon the coarse-grained CF, enabling the CGDM to learn the complicated distribution of the target data. During the denoising neural network optimization, the coarse-grained CF is introduced asside informationto accurately guide the conditioned generation of the CGDM. To make the proposed CGDM lightweight, we further leverage the additivity of output distortion and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique. Experimental results show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines. Additionally, zero-shot testing on reconstruction tasks with different magnification factors further demonstrates the scalability and generalization ability of the proposed approach.

In this work, a multiple waveguide pinching antenna system (PASS) assisted integrated sensing and communication (ISAC) system is proposed, where the base station (BS) is equipped with transmitting pinching antennas (PAs) and receiving uniform linear array (ULA) antennas. The PASS-transmitting- ULA-receiving (PTUR) BS transmits the communication and sensing signals through the PAs on waveguides and collects the echo sensing signals with the mounted ULA. Based on this configuration, a target sensing Cramèr–Rao Bound (CRB) minimization problem is formulated under communication quality-of-service (QoS) constraints, power budget constraint, and PA deployment constraints. To tackle the resulting non-convex problem, an alternating optimization (AO) framework is developed, which decomposes the problem into a digital beamforming sub-problem and a pinching beamforming sub-problem. The digital beamforming design is optimized via semidefinite relaxation (SDR), while the PA deployment is updated using penalty-based method. Simulation results demonstrate that: 1) the proposed PASS assisted ISAC framework achieves superior performance over benchmark schemes; and 2) the PASS assisted ISAC is less affected by stringent communication constraints compared to conventional MIMO-ISAC, and benefits from increasing the number of waveguides and PAs per waveguide.

As a foundational technology for the sixth generation networks that integrates communication, sensing, computing, and intelligence, integrated sensing and communication (ISAC) surpasses conventional isolated system designs across multiple performance dimensions. Given its superior interference management capability, non-orthogonal multiple access (NOMA) presents significant potential for integration into ISAC systems. To this end, this paper establishes a unified NOMA-ISAC framework that concurrently incorporates residual hardware impairments, channel estimation errors, and imperfect successive interference cancellation (SIC). Within this framework, we propose two distinct SIC designs tailored for different operational priorities: a communication-centric design (CCD) and a sensing-centric design (SCD), thereby introducing SIC ordering as a new dimension for managing the sensing-communication trade-off. For both proposed designs, we derive analytical expressions encompassing exact and asymptotic lower bounds of outage probabilities, ergodic communication rates for users, as well as probability of detection (PoD), probability of false alarm, and sensing sum rate for the base station. These analytical results provide a theoretical foundation for optimizing critical system parameters, such as power allocation and beamforming design. Our analysis reveals the synergistic effect of these impairments, leading to a simultaneous error floor in both communication and sensing performance. Notably, the proposed SIC ordering enables substantial performance gains in the prioritized domain: the CCD scheme improves ECRs by over 40%, whereas the SCD scheme enhances target PoD by more than 25%, under practical impairment conditions.

This article investigates the beam training design for pinching-antenna systems (PASS) in the near-field communication region, where single-waveguide-single-user (SWSU), single-waveguide-multi-user (SWMU) and multi-waveguide-multi-user (MWMU) scenarios are considered. For SWSU-PASS, we design a scalable codebook, based on which we propose a three-stage beam training (3SBT) scheme. Specifically, 1) firstly, the 3SBT scheme utilizes one activated pinching antenna to obtain a coarse one-dimensional location at the first stage; 2) secondly, it achieves further phase matching with an increased number of activated antennas at the second stage; 3) finally, it realizes precise beam alignment through an exhaustive search at the third stage. For SWMU-PASS, based on the scalable codebook design, we propose an improved 3SBT scheme to support non-orthogonal multiple access (NOMA) transmission. For MWMU-PASS, we first present a generalized expression of the received signal based on the partially-connected hybrid beamforming structure. Furthermore, we introduce an increased-dimensional scalable codebook design, based on which an increased-dimensional 3SBT scheme is proposed. Numerical results reveal that: i) the proposed beam training schemes can significantly reduce the training overhead compared to the two-dimensional exhaustive search, while maintaining the same training accuracy; ii) PASS yields better flexibility and improved performance compared to several benchmark schemes.

Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) have emerged as two promising technologies for enhancing wireless communication performance, owing to their capability to dynamically reshape and manipulate the propagation environment. Motivated by this potential, this paper investigates the joint utilization of the additional degrees of freedom introduced by MAs (through antenna repositioning) and RIS (via optimized reflection) to effectively mitigate computation distortion in over-the-air computation (AirComp) systems. Specifically, we formulate an optimization problem aimed at minimizing the mean square error (MSE) between the target function values and their estimates, through jointly optimizing the receive beamformer at the access point, RIS reflection phase shifts, and transmit coefficients as well as antenna positions of AirComp users. To address the non-convex nature of the formulated problem, we develop a computationally efficient algorithm capitalizing alternating optimization technique, the penalty-dual decomposition method, and the particle swarm optimization enhanced by a dynamic neighborhood pruning mechanism. Next, we further extend the optimization framework to a more practical case with discrete MA positions. Extensive simulation results demonstrate that the joint optimization of RIS beamforming and MA positioning substantially reduces the computation MSE, compared to the separate MA-enhanced AirComp and RIS-aided AirComp schemes. Moreover, the proposed algorithm achieves comparable performance to the penalty function-based method, while incurring significantly lower computational complexity.

In this paper, we introduce a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) model designed for non-line-of-sight (NLoS) scenarios. To ensure energy self-sustainability, an unmanned aerial vehicle (UAV) is introduced for wireless energy transfer. In the proposed model, ground users (GUs) situated in communication-obstructed environments are supported by STAR-RIS to connect with the base station (BS). Energy harvested from the UAV is utilized to enable prolonged communication with 360° full spatial coverage. An optimization problem is formulated to maximize the system’s sum-rate and is decomposed into three subproblems: phase-shift optimization, power allocation, and time allocation. These subproblems are solved using semidefinite relaxation (SDR), Dinkelbach’s method, and game theory, respectively. A joint resource allocation algorithm based on the block coordinate descent (BCD) method is then proposed. Simulation results show that the proposed UAV-assisted STAR-RIS-NOMA scheme, combined with the BCD algorithm, achieves a 43.64% improvement in system capacity compared to existing approaches.

A near-field motion parameter estimation method is proposed. In contrast to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cramér-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications.

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

Unlike conventional systems using a fixed-location antenna, the channel capacity of the pinching-antenna system (PASS) is determined by the activated positions of pinching antennas. This article characterizes the capacity region of multiuser PASS, where a single pinched waveguide is deployed to enable both uplink and downlink communications. The capacity region of the uplink channel is first characterized. i) For the single-pinch case, closed-form expressions are derived for the optimal antenna activation position, along with the corresponding capacity region and the achievable data rate regions under time-division multiple access (TDMA) and frequency-division multiple access (FDMA). It is proven that the capacity region of PASS encompasses that of conventional fixed-antenna systems, and that the FDMA rate region contains the TDMA rate region. ii) For the multiple-pinch case, inner and outer bounds on the capacity region are derived using an element-wise alternating antenna position optimization technique and the Cauchy-Schwarz inequality, respectively. The achievable FDMA rate region is also derived using the same optimization framework, while the TDMA rate region is obtained through an antenna position refinement approach. The analysis is then extended to the downlink PASS using the uplink-downlink duality framework. It is proven that the relationships among the downlink capacity and rate regions are consistent with those in the uplink case. Numerical results demonstrate that: i) the derived bounds closely approximate the exact capacity region, ii) PASS yields a significantly enlarged capacity region compared to conventional fixed-antenna systems, and iii) in the multiple-pinch case, TDMA and FDMA are capable of approaching the channel capacity limit.

The performance of linear receive beamforming in continuous-aperture array (CAPA)-based uplink communications is analyzed. Three continuous beamforming techniques are proposed under the criteria of maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-squared error (MMSE). i) ForMRC beamforming, a closed-form expression for the beamformer is derived to maximize per-user signal power. The achieved uplink rate and mean-squared error (MSE) in detecting received data symbols are analyzed. ii) ForZF beamforming, a closed-form beamformer is derived based on channel correlation to eliminate interference. As a further advance, its optimality in maximizing effective channel gain while ensuring zero inter-user interference is proven. iii)MMSE beamformingis established as the optimal linear receive approach for CAPAs in terms of maximizing per-user rate and minimizing MSE. Closed-form expressions are derived for the MMSE beamformer and the achievable sum-rate and sum-MSE. It is mathematically proven that all proposed beamformers lie within the signal subspace spanned by users’ spatial responses. Numerical results demonstrate that CAPAs outperform conventional spatially-discrete arrays (SPDAs) by achieving higher sum-rates and lower sum- MSEs under the proposed linear beamforming techniques.

The pinching-antenna system (PASS) reconstructs wireless channels through pinching beamforming, i.e., optimizing the activated locations of pinching antennas (PAs) along the waveguide. The aim of this article is to investigate the joint design of baseband beamforming and pinching beamforming. A low-complexity element-wise sequential optimization framework is proposed to address the sum-rate maximization problem in PASS-enabled downlink and uplink channels. i) For the downlink scenario, maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean square error (MMSE) beamforming schemes are employed as baseband beamformers. For each beamformer, a closed-form expression for the downlink sum-rate is derived as a single-variable function with respect to the pinching beamformer. Based on this, a sequential optimization method is proposed, where the positions of the PAs are updated element-wise using a low-complexity one-dimensional search. ii) For the uplink scenario, signal detection is performed using maximum ratio combining (MRC), ZF, and MMSE combiners. A closed-form sum-rate expression is derived for each linear combiner, and a similar element-wise design is applied to optimize the pinching beamforming. Numerical results are provided to validate the effectiveness of the proposed method and demonstrate that: (i) For all considered linear beamformers, the proposed PASS architecture outperforms conventional fixed-antenna systems in terms of sum-rate performance; (ii) in both downlink and uplink channels, ZF achieves performance close to that of MMSE and significantly outperforms MRT or MRC; and (iii) the proposed element-wise design eliminates the need for alternating updates between the baseband and pinching beamformers, thereby ensuring low computational complexity.

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial degrees of freedom (DoFs) and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on zero-forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a water-filling approach to design an optimal power allocation scheme that maximizes the WSSR. In high signal-to-noise ratio regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity.

An efficient beamforming design is proposed for continuous aperture array (CAPA)-based point-to-point multiple-input multiple-output (MIMO) systems. In contrast to conventional spatially discrete array (SPDA)-MIMO systems, whose optimal beamforming can be obtained using singular-value decomposition, CAPA-MIMO systems require solving the eigendecomposition of a Hermitian kernel operator, which is computationally prohibitive. To address this challenge, an explicit closed-form expression for the achievable rate of CAPA-MIMO systems is first derived as a function of the continuous transmit beamformer. Subsequently, an iterative weighted minimum mean-squared error (WMMSE) algorithm is proposed, directly addressing the CAPA-MIMO beamforming optimization without discretization approximation. Closed-form updates for each iteration of the WMMSE algorithm are derived via the calculus of variations (CoV) method. For low-complexity implementation, an equivalent matrix-based iterative solution is introduced using Gauss-Legendre quadrature. Our numerical results demonstrate that 1) CAPA-MIMO achieves substantial performance gain over the SPDA-MIMO, 2) the proposed WMMSE algorithm enhances performance while significantly reducing computational complexity compared to state-of-the-art Fourier-based approaches, and 3) the proposed WMMSE algorithm enables practical realization of parallel, non-interfering transmissions.

The phenomenon of mutual coupling in continuous aperture arrays (CAPAs) is studied. First, a general physical model for the phenomenon that accounts for both polarization and surface dissipation losses is developed. Then, the unipolarized coupling kernel is characterized, revealing that polarization induces anisotropic coupling and invalidates the conventional half-wavelength spacing rule for coupling elimination. Next, the beamforming design problem for CAPAs with coupling is formulated as a functional optimization problem, leading to the derivation of optimal beamforming structures via the calculus of variations. To address the challenge of inverting the coupling kernel in the optimal structure, two methods are proposed: 1) the kernel approximation method, which yields a closed-form solution via wavenumber-domain transformation and GaussLegendre quadrature, and 2) the conjugate gradient method, which addresses an equivalent quadratic functional optimization problem iteratively. Furthermore, the optimal array gain and beampattern are analyzed at the large-aperture limit. Finally, the proposed continuous mutual coupling model is extended to spatially discrete arrays (SPDAs), and comprehensive numerical results are provided, demonstrating that: 1) coupled SPDA performance correctly converges to the CAPA limit, while uncoupled models are shown to violate physics, 2) polarization results in anisotropic array gain behavior, and 3) the coupled beampattern exhibits higher directivity than the uncoupled beampattern.

In fog radio access networks (F-RANs), caching popular content at edge fog access points (FAPs) helps alleviate the burden on base stations and backhaul links. To improve signal quality and connectivity, this work integrates cooperative communication and non-orthogonal multiple access (NOMA) into content-centric F-RANs with unreliable backhauls. Specifically, a NOMA-enabled joint transmission scheme is considered, where cache-enabled FAPs are coordinated into clusters to perform non-coherent joint transmission and NOMA, enabling multiplexing signals for multiple users. Under a hybrid caching policy and non-uniform Nakagami-mfading channels, the system performance is analyzed in terms of the successful content delivery probability and outage achievable rate. To optimize FAP coordination, the clustering problem is formulated as a coalitional game, and a low-complexity transfer-based clustering algorithm is designed. Furthermore, a hierarchical hybrid NOMA-based algorithm is developed to enhance multi-user access efficiency. Simulation results demonstrate that: 1) The NOMA-enabled joint transmission scheme allows the FAPs efficiently leverage the cached content to mitigate backhaul unreliability; 2) The NOMA-based design outperforms the orthogonal multiple access-based design by guaranteeing improved signal quality while maintaining the efficiency of multi-user access; 3) The coalitional game-based clustering algorithm effectively manages co-channel interference and improves spectrum utilization.

Hybrid non-orthogonal multiple access (H-NOMA) technology, which combines the benefits of non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) through flexible resource allocation in a single transmission, has shown great potential for enhancing the performance of wireless communication systems. To further exploit the potential of H-NOMA, this paper proposes a novel design of H-NOMA which jointly incorporates hybrid successive interference cancellation (HSIC) and power adaptation (PA) in the NOMA transmission phase, by introducing a power adaptation factor . For a given power reducing coefficient β, which ensures that the energy consumption of the proposed scheme is lower than that of conventional OMA, the probability that the achievable rate of the proposed HSIC-PA aided H-NOMA scheme fails to outperform its OMA counterpart is derived in closed form. Besides, the impact of user pairing is considered. Furthermore, the asymptotic analysis shows that the aforementioned probability of the proposed H-NOMA scheme can approach zero in the high signal-to-noise ratio (SNR) regime without constraints on either users’ target rates or transmit power. By dynamically adjusting the transmission power of the opportunistic user and the decoding order of HSIC, signal interference between the legacy user and the opportunistic user can be effectively controlled, thereby improving the achievable rate and energy efficiency of the opportunistic user. This represents a significant improvement over conventional H-NOMA schemes, which require specific restrictive conditions to make the probability that their achievable rate underperforms OMA approach zero at high SNR, as shown in existing work. The above observation indicates that, with lower energy consumption, the proposed HSIC-PA aided H-NOMA can achieve a higher data rate than pure OMA with probability 1 at high SNR, leading to improved energy efficiency. Finally, numerical results are provided to verify the accuracy of the analysis and also to demonstrate the superior performance of the proposed H-NOMA scheme.

In this paper, sensing-assisted secure communication in a multi-user multi-eavesdropper integrated sensing and communication (ISAC) system is investigated. Confidential communication signals and dedicated sensing signals are jointly transmitted by a base station (BS) to simultaneously serve users and sense aerial eavesdroppers (AEs). A sum rate maximization problem is formulated under AEs’ Signal-to-Interference-plus-Noise Ratio (SINR) and sensing Signal-to-Clutter-plus-Noise Ratio (SCNR) constraints. A fractional-programming-based alternating optimization algorithm is developed to solve this problem for fully digital arrays, where successive convex approximation (SCA) and semidefinite relaxation (SDR) are leveraged to handle non-convex constraints. Furthermore, the minimum number of dedicated sensing beams is analyzed via a worst-case rank bound, upon which the proposed beamforming design is further extended to the hybrid analog-digital (HAD) array architecture, where the unit-modulus constraint is addressed by manifold optimization. Simulation results demonstrate that only a small number of sensing beams are sufficient for both sensing and jamming AEs, and the proposed designs consistently outperform strong baselines while also revealing the communication–sensing trade-off.

Fluid antenna system (FAS) is regarded as one of the key enabling technologies for supporting massive communications in next-generation networks, owing to its advantages of compact design, low power consumption, and significant performance gains. This paper investigates a FAS-assisted downlink non-orthogonal multiple access (NOMA) satellite network, where terminal users are equipped with FAS to receive superimposed signals from the satellite. The satellite-to-FAS channels are modeled as spatially correlated shadowed-Rician fading distributions, where the spatial correlation matrix is obtained from the von Mises–Fisher model. By utilizing block-correlated approximation method, the cumulative distribution function of the maximum spatially correlated shadowed-Rician fading channel gain is derived. On this basis, closed-form expressions for outage probability and ergodic data rate of the FAS-NOMA satellite network are derived under both imperfect and perfect successive interference cancellation scenarios. Moreover, asymptotic expressions for outage probability and ergodic data rate of the FAS-NOMA satellite network are derived in the high signal-to-noise ratio region to unveil the achievable diversity gain and multiplexing gain. Simulation results reveal that: 1) Compared to conventional antenna system with selection combining scheme, FAS can effectively exploit the fluctuations of the channel response and provide additional selection diversity, thereby achieving superior performance in NOMA satellite networks; and 2) By adjusting the power allocation factor under different antenna apertures, the performance of FAS-NOMA satellite networks can be further enhanced.

A novel mobile edge generation (MEG) framework is proposed to efficiently operate large models at edge networks for low-latency image generation. The generation of large-scale image content is split into two parts, namely primary and secondary regions, with an adjustable generation splitting ratio. The primary region is generated by a large generative model (LGM) at the edge cloud and then transmitted to the mobile device, whereas the remaining secondary regions is created by a tiny generative model (TinyGM) at the mobile device, thus reducing transmission and computation overheads. Both single-user and multi-user cases are considered to characterize the tradeoff between mobile energy consumption and generation delay. For the single-user case, a multi-objective programming (MOP) is formulated for the joint optimization of generation splitting and mobile power control, which simultaneously minimizes the generation delay and mobile energy consumption. This MOP is transferred into single-objective optimization using the ϵ-constraint method. The closed-form optimal solution is derived to obtain Pareto-optimal energy-delay (E-D) region. It is revealed that MEG achieves significant performance gains then conventional fully edge generation (FEG) when signal-to-noise ratio (SNR) or mobile generative cost is low. For the multi-user case, a joint generation splitting and resource allocation problem is formulated, which minimizes the maximum generation delay subject to ϵ-bounded mobile energy consumption and resource constraints. An McCormick-relaxation branch-and-bound (M-BnB) algorithm is proposed to obtain the globally optimal solution. Simulation results demonstrate the Pareto-optimal E-D region in single-user and multi-user cases. Furthermore, MEG flexibly reduces delay compared to conventional FEG and model split schemes while maintaining generative quality.

A novel pinching antenna system (PASS)-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. PASS consists of multiple waveguides spanning over thousands of wavelength, which equip numerous low-cost dielectric particles, named pinching antennas (PAs), to radiate signals into free space. The positions of PAs can be reconfigured to change both the large-scale path losses and phases of signals, thus facilitating the novelpinching beamformingdesign. A sum rate maximization problem is formulated, which jointly optimizes the transmit and pinching beamforming to adaptively achieve constructive signal enhancement and destructive interference mitigation. To solve this highly coupled and nonconvex problem, both optimization-based and learning-based methods are proposed. 1) For the optimization-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed, which handles the nonconvex complex exponential component using a Lipschitz surrogate function and then invokes PDD for problem decoupling. 2) For the learning-based method, a novel Karush-Kuhn-Tucker (KKT)-guided dual learning (KDL) approach is proposed, which enables KKT solutions to be reconstructed in a data-driven manner by learning dual variables. Following this idea, a KDL-Transformer algorithm is developed, which captures both inter-PA/inter-user dependencies and channel-state-information (CSI)-beamforming dependencies by attention mechanisms. Simulation results demonstrate that: i) The proposed PASS framework significantly outperforms conventional massive multiple input multiple output (MIMO) system even with a few PAs. ii) The proposed KDL-Transformer can improve over 20% system performance than MM-PDD algorithm, while achieving a millisecond-level response on modern GPUs.

A simultaneously transmitting and reflecting surface (STARS) assisted near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the radio sensors are installed on the STARS to directly conduct the distance-domain sensing by exploiting the spherical wavefront. A new squared position error bound (SPEB) expression is derived to reveal the dependence on beamforming (BF) design and sensor deployment. To balance the trade-off between the SPEB and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the sensor deployment, the active and passive BF, subject to communication and power consumption constraints. For the sensor deployment optimization, a joint sensor deployment algorithm is proposed by invoking the successive convex approximation. Under a specific relationship between the sensor numbers and BF design, we derive the optimal sensor interval in a closed-form expression. For the joint BF optimization, a penalty-based method is invoked. Simulation results validated that the derived SPEB expression is close to the exact SPEB, which reveals the Fisher Information Matrix of position estimation in NF can be approximated as a diagonal matrix. Furthermore, the proposed algorithms achieve the best SPEB performance compared to the benchmark schemes accompanying the lowest deployment cost.

Stacked intelligent metasurface (SIM) and dual-polarized SIM (DPSIM) enabled wave-domain signal processing have emerged as promising research directions for offloading baseband digital processing tasks and efficiently simplifying transceiver design. However, existing architectures are limited to employing SIM (DPSIM) for a single communication function, such as precoding or combining. To further enhance the overall performance of SIM (DPSIM)-assisted systems and achieve end-to-end (E2E) joint optimization from the transmitted bitstream to the received bitstream, we propose an SIM (DPSIM)-assisted E2E orthogonal frequency division multiplexing (OFDM) system, in which traditional communication tasks such as channel coding, modulation, precoding, combining, demodulation, and channel decoding are performed synchronously within the electromagnetic (EM) forward propagation. Furthermore, inspired by the idea of abstracting real metasurfaces as hidden layers of a neural network, we propose the EM neural network (EMNN) to enable the control of the E2E OFDM communication system. In addition, transfer learning is introduced into the model training, and a training and deployment framework for the EMNN is designed. Simulation results demonstrate that both SIM-assisted E2E OFDM systems and DPSIM-assisted E2E OFDM systems can achieve robust bitstream transmission under complex channel conditions. Our study highlights the application potential of EMNN and SIM (DPSIM)-assisted E2E OFDM systems in the design of next-generation transceivers.

An analytical and optimization framework on rate-Cramér-Rao bound (CRB) tradeoff is proposed in this paper for the continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) system. To evaluate the dual-functional performance, the sensing CRB and communication rate are analyzed concerning the induced electromagnetic (EM) waves of CAPAs. For rate-CRB region characterization, the spatially continuous beamforming of transmit CAPA is optimized under three cases: i) A novel closed-form expression for the optimal CAPA beamformer is derived under the single-user single-target scenario, proven to be aligned within the space spanned by the EM-based sensing and communication channels; ii) A general subspace-based beamforming design approach is proposed to address the intractable continuity, converting the continuous beamforming design in spatial domain to discrete weight design in subspace domain and resorting to the semidefinite relaxation for the globally optimal solution; iii) Moreover, the general beamforming design is specialized to both the low-complexity zero-forcing (ZF) and the conventional spatially discrete array (SPDA)-based designs. Numerical results demonstrate that: i) The proposed subspace-based approach can realize efficient and effective beamforming design for reduced mutual interference, enhanced sensing performance, and guaranteed communication rate; ii) The general CAPA beamforming design achieves broader rate-CRB region than the ZF-oriented design and reaches the ultimate performance of the SPDA-based system.

A novel mobile edge generation (MEG) framework is proposed to support latency-sensitive generation tasks in the social-aware device-to-device (D2D) underlaying cellular network. Within this framework, user devices (UDs) and base station (BS) are organized into socially cohesive communities based on content preference and spatial proximity, enabling cooperative generation tasks via both cellular and intra-community D2D communications. A joint seed-and-content based BS-D2D (JSCB) transmission protocol is proposed to dynamically orchestrate the transmission mode between seed acquisition with local generation and direct content sharing across multiple consecutive task rounds, incorporating the spillover mechanism for handling overdue transmissions. Based on this protocol, an average task delay minimization problem is formulated to jointly optimize the UD association between cellular and D2D communication, transmission mode, D2D pairing, and BS-side beamforming. To efficiently solve the hybrid and temporally coupled problem, a joint matching and proximal policy optimization (JMPPO) algorithm is developed, where the discrete and continuous actions are decoupled with specialized modules though a hierarchical deep reinforcement learning and matching design. Numerical results validate that 1) the JSCB protocol reduces delay through adaptive transmission scheduling and cellular/D2D coordination; 2) the JMPPO algorithm outperforms both learning-based and traditional baselines in terms of average delay under the spillover and hybrid action scenarios; 3) the proposed schemes demonstrate robustness across diverse network and system conditions.

A continuous-aperture array (CAPA)-based secure transmission framework is proposed to enhance physical layer security. Continuous current distributions, or beamformers, are designed to maximize the secrecy transmission rate under a power constraint and to minimize the required transmission power for achieving a specific target secrecy rate. On this basis, the fundamental secrecy performance limits achieved by CAPAs are analyzed by deriving closed-form expressions for the maximum secrecy rate (MSR) and minimum required power (MRP), along with the corresponding optimal current distributions. To provide further insights, asymptotic analyses are performed for the MSR and MRP, which reveals that i) for the MSR, the optimal current distribution simplifies to maximal ratio transmission (MRT) beamforming in the low-SNR regime and to zero-forcing (ZF) beamforming in the high-SNR regime; ii) for the MRP, the optimal current distribution simplifies to ZF beamforming in the high-SNR regime. The derived results are specialized to the typical array structures, e.g., planar CAPAs and planar spatially discrete arrays (SPDAs). The rate and power scaling laws are further analyzed by assuming an infinitely large CAPA. Numerical results demonstrate that: i) the proposed secure continuous beamforming design outperforms MRT and ZF beamforming in terms of both achievable secrecy rate and power efficiency; ii) CAPAs achieve superior secrecy performance compared to conventional SPDAs.

A continuous-aperture array (CAPA)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. Within this framework, continuous operator-based signal models are employed to describe the sensing and communication processes. The performance of communication and sensing is analyzed using two information-theoretic metrics: the communication rate (CR) and the sensing rate (SR). 1) For downlink ISAC, three continuous beamforming designs are proposed: i) the communications-centric (C-C) design that maximizes the CR, ii) the sensing-centric (S-C) design that maximizes the SR, and iii) the Pareto-optimal design that characterizes the Pareto boundary of the CR-SR region. A low-complexity signal subspace-based approach is proposed to derive the closed-form optimal beamformers for the considered designs. On this basis, closed-form expressions are derived for the achievable CRs and SRs, and the downlink rate region achieved by CAPAs is characterized. 2) For uplink ISAC, the C-C and S-C successive interference cancellation-based methods are proposed to manage inter-functionality interference. Using the subspace approach closed-form expressions for the optimal detectors as well as the achievable CRs and SRs are derived. The uplink SR-CR region is characterized based on the time-sharing technique. Numerical results demonstrate that, for both downlink and uplink, CAPA-based ISAC achieves higher CRs and SRs as well as larger CR-SR regions compared to conventional spatially discrete array-based ISAC.

A novel movable-element enabled simultaneously transmitting and reflecting surface (ME-STARS) communication system is proposed, where ME-STARS elements positions can be adjusted to enhance the degress-of-freedom for transmission and reflection. For each ME-STARS operating protocols, namely energy-splitting (ES), mode switching (MS), and time switching (TS), a weighted sum rate (WSR) maximization problem is formulated to jointly optimize the active beamforming at the base station (BS) as well as the elements positions and passive beamforming at the ME-STARS. An alternative optimization (AO)-based iterative algorithm is developed to decompose the original non-convex problem into three subproblems. Specifically, the gradient descent algorithm is employed for solving the ME-STARS element position optimization subproblem, and the weighted minimum mean square error and the successive convex approximation methods are invoked for solving the active and passive beamforming subproblems, respectively. It is further demonstrated that the proposed AO algorithm for ES can be extended to solve the problems for MS and TS. Numerical results unveil that: 1) the ME-STARS can significantly improve the WSR compared to the STARS with fixed position elements and the conventional reconfigurable intelligent surface with movable elements, thanks to the extra spatial-domain diversity and the higher flexibility in beamforming; and 2) the performance gain of ME-STARS is significant in the scenarios with larger number of users or more scatterers.

Previous physical adversarial attacks have shown that carefully crafted perturbations can deceive face recognition systems, revealing critical security vulnerabilities. However, these attacks often struggle to impersonate multiple targets and frequently fail to bypass liveness detection. For example, attacks using human-skin masks [28] are challenging to fabricate, inconvenient to swap between users, and often fail liveness detection due to facial occlusions. A projector, however, can generate content-rich light without obstructing the face, making it ideal for non-intrusive attacks. Thus, we propose a novel physical adversarial attack using a projector and explore the superposition of projected and natural light to create adversarial facial images. This approach eliminates the need for physical artifacts on the face, effectively overcoming these limitations. Specifically, our proposed ProjAttacker generates adversarial 3D textures that are projected onto human faces. To ensure physical realizability, we introduce a light reflection function that models complex optical interactions between projected light and human skin, accounting for reflection and diffraction effects. Furthermore, we incorporate camera Image Signal Processing (ISP) simulation to maintain the robustness of adversarial perturbations across real-world diverse imaging conditions. Comprehensive evaluations conducted in both digital and physical scenarios validate the effectiveness of our method.

A comprehensive performance analysis is conducted for pinching-antenna systems (PASS) under non-orthogonal multiple access (NOMA) transmission. Specifically, a downlink scenario is investigated, in which a pinching antenna is dynamically activated along a dielectric waveguide to serve two users located in separate rooms. The wireless links between the pinching antenna and the users are modeled using a line-of-sight (LoS) and non- line-of-sight (NLoS) propagation conditions, respectively. Closed- form expressions are derived for the outage probabilities (OPs) of the two users. Furthermore, asymptotic analyses in the high signal-to-noise ratio (SNR) regime are conducted to reveal the achievable diversity orders. Numerical simulations validate the accuracy of the theoretical analysis and demonstrate that: 1) Compared with conventional antenna systems (CASS), the OP of the LoS user in PASS is significantly reduced in the middle SNR regime and approaches zero as SNR increases; 2) Since the diversity orders of the NLoS user in CASS and PASS are the same, the movement of the pinching antenna has no significant effect on the OP of the NLoS user.

In this paper, a general ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning algorithms to provide a three-stage solution for ISAC beamforming. The three-stage beamforming optimization solution includes three modules: 1) an unsupervised learning based feature extraction algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL agent. Simulation results demonstrate that the proposed three-stage solution outperforms the baseline RL algorithm by optimizing the intuitional beampattern rather than beamforming.

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, multiple PAs are deployed for a single user, namely MPSU. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. Our results demonstrate the following key insights: i) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; ii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

The performance of linear receive beamforming in continuous-aperture array (CAPA)-based uplink communications is investigated. Three continuous beamforming strategies are proposed based on the principles of maximum-ratio combining (MRC), zero-forcing (ZF), and maximum signal-to-interference-plus-noise ratio (SINR) (i.e., optimal beamforming). For MRC beamforming, closed-form expressions for both the beamformer and the achievable sum-rate are derived. For ZF beamforming, a closed-form solution is developed using channel correlation to effectively eliminate inter-user interference. For optimal beamforming, a closed-form beamformer is obtained by solving an operator-based Rayleigh quotient maximization problem, and the associated achievable sum-rate is characterized. Numerical results confirm that CAPAs outperform traditional spatially-discrete arrays (SPDAs), achieving superior sum-rate performance under all three beamforming schemes.

This paper studies the application of pinching-antenna systems (PASS) in multicast communications. The minimum-rate maximization problem is formulated, and both single-pinching antenna (PA) and multiple-PA deployment over a single waveguide are considered. 1) For the single-PA scenario, a closed-form solution is derived for one-dimensional user distributions, and a candidate-point search strategy is proposed for arbitrary user placements. 2) For the multiple-PA scenario, the pinching beamforming is optimized through a low-complexity greedy search approach. Numerical results demonstrate that: i) PASS achieves a higher multicast rate compared to conventional fixed-antenna systems; ii) the proposed approaches substantially reduce computational complexity compared to traditional exhaustive search algorithms.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. A FAS possesses position reconfigurability to switch on-demand among$N$predefined ports over a prescribed space. This paper explores the performance of a singleinput single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closedform bounds for the outage probability of the Rx-SISO-FAS model with uniform linear array port configuration. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,$N$, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:$i$) the Rx-SISO-FAS model can enhance performance under spatiallycorrelated Rician fading channels over the fixed-position antenna counterpart;$i i)$the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime.

The application of continuous aperture array (CAPA) for mono-static sensing is studied in this paper. Specifically, the transmit CAPA emits a probing signal to a sensing target (ST) and then positions this ST using the reflected echo signal from the ST. To evaluate the sensing performance, the CramérRao Bound (CRB) is derived according to the proposed roundtrip channel model based on electromagnetic theory. Moreover, a maximum likelihood detection scheme is proposed to position the ST under the maximum likelihood criteria. Simulation results demonstrate the high accuracy of CAPA-enabled mono-static sensing.

Terahertz (THz) band is acknowledged as a candidate spectrum in future networks. However, THz communications demand the assistance of high-accuracy parameter estimations to achieve narrow beam-alignment. Despite existing works have intensively explored sensing-assisted THz communications, they fail in mobile and near-field scenarios without concerning the benefits brought from velocity and distance estimations. To this end, a closed-form framework toward sensing-assisted terahertz near-field mobile communications (SA-TNMC) is introduced in this paper. Four-dimensional parameters containing distance, azimuth angle, radial velocity, and tangential angular velocity are extracted from the echoes, all of which are utilized for beamfocusing. For performance characterizations, both Cramér-Rao Bound and Ergodic Shannon Capacity are derived in closedform expressions. Besides, a novel critical point is proposed to demarcate the minimized resource division factor to activate SA-TNMC system. At last, numerical results are proceeded to validate the framework.

A subarray-based near-field joint location and velocity estimation framework is proposed for sensing a moving target using extremely large-scale antenna arrays. To tackle the intricate near-field non-linear phase, the piecewise-far-field channel model is adopted, approximating the near-field channel by partitioning the transmit and receive arrays into subarrays and applying the near-field assumption between subarrays and the far-field assumption within each subarray. Based on this model, the complex near-field estimation problem can be transformed into a far-field joint multiple bistatic radar parameter estimation problem, enabling separable location and velocity estimation. An efficient three-stage algorithm is developed, exploiting joint sparsity across transmit-receive subarray pairs. In the first stage, a mixed-norm minimization method is employed to obtain coarse estimates of the location and complex channel gain, which are refined using the gradient descent method in the second stage. Finally, the velocity is estimated using the multiple signal classification spectrum estimation method, based on the refined location estimate. Simulation results demonstrate the effectiveness of the proposed framework and reveal a trade-off in system design: location estimation accuracy improves with increased subarray size, while velocity estimation benefits from a greater number of smaller subarrays.

The diversity and multiplexing performance of continuous aperture array (CAPA)-based multiple-input multiple-output (MIMO) channels is analyzed. Angular-domain fading models are derived, and an angular-domain transmission framework is proposed to support CAPA-based MIMO communications. Asymptotic expressions are derived for the achievable outage probability (OP) and average data rate (ADR), and insights into the diversity-multiplexing trade-off (DMT) and associated array gain are provided. Further, the performance of CAPAs is compared with that of conventional spatially discrete arrays (SPDAs) to highlight the advantages of CAPAs. Analytical and numerical results demonstrate that: i) CAPAs achieve lower OP and higher ADR than SPDAs; ii) CAPAs attain the same DMT as SPDAs with half-wavelength antenna spacing but with a higher array gain; and iii) CAPAs outperform SPDAs in DMT when the antenna spacing exceeds half a wavelength.

The beamforming optimization for maximizing weighted sum-rate (WSR) in continuous aperture array (CAPA)-based multi-user communications is studied. In particular, the transmit beamformers of CAPA are modelled as continuous source current patterns, rendering the beamforming optimization problem as a non-convex integral-based functional programming problem. In contrast to the state-of-the-art Fourier-based method that requires numerous Fourier basis functions to approximate the functional programming, a low-complexity calculus of variations (CoV)-based method is proposed to solve the functional programming problem for WSR maximization directly, where the optimal form of the continuous source patterns is derived. Based on this optimal form, a low-complexity integral-free iterative algorithm is developed. Our numerical results validate the effectiveness of the proposed designs. It is revealed that compared to the state-of-the-art Fourier-based method, the proposed CoV based method not only improves WSR performance but also reduces computational complexity by up to hundreds of times for large CAPA apertures and high frequencies.

A novel mobile edge generation (MEG) framework is proposed to enable large generative model (LGM) capabilities at the edge, which offers low-latency, power-saving, and languageguided generation on mobile device. Specifically, our framework splits the generation of large-scale content (e.g., high-definition image) into two parts, namely primary and secondary regions. Only the primary region is generated by the LGM at the edge cloud and transmitted via downlink, while the secondary region is generated by the tiny generative model (TinyGM) at the mobile device. By configuring generation splitting ratio between edge and mobile devices, the transmission and computation overheads can be reduced. We formulate a joint generation splitting and mobile power control optimization problem. The formulated problem is a multi-objective optimization programming, which simultaneously minimizes the generation latency and the mobile energy consumption. To explore the performance limits, we first transfer the multi-objective programming into a single-objective programming based on the$\epsilon$-constraint method. Then, we derive the closed-form Pareto-optimal solution of generation splitting and mobile power control. Thereby, the performance boundary of energy-delay (E-D) tradeoff region is obtained. Furthermore, we also identify the conditions under which the proposed MEG strictly outperforms the fully edge generation (FEG) scheme, and demonstrates that performance gains increase as signal-tonoise ratio (SNR) and mobile generation cost decrease. Numerical results demonstrate the optimal E-D tradeoff of the proposed MEG and verify that it can significantly reduce the latency compared to FEG while achieving satisfactory generation.

Enabled by metamaterials, continuous aperture array (CAPA) has been proven to play a crucial role in communication performance enhancement, while its potentials in integrated sensing and communication (ISAC) systems have not been investigated. This paper investigates the performance analysis and optimization of CAPA-based ISAC systems for simultaneous user communication and target sensing. To be specific, communication and sensing rates are evaluated based on electromagnetic channels and a Pareto-optimal problem is formulated for beamforming optimization. Closed-form solutions to CAPA-oriented beamforming are derived under communication-, sensing-, and Pareto-optimal cases, and the attainable ISAC rate region is obtained. Numerical results verify that CAPA-based systems can achieve the ultimate sensing and communication performance of spatially discrete array (SPDA)-based systems and significantly expand the ISAC rate region for Pareto optimality.

A framework of holographic MIMO (HMIMO) based integrated sensing and communications (ISAC), i.e., holographic ISAC (HISAC), is proposed. An accurate spherical wave-based model is utilized to characterize sensing link. The spacial correlation introduced by the densely spaced antennas of the HMIMO is incorporated when modeling the communication channel. Based on the proposed framework, closed-form expressions are derived for sensing rates (SRs), communication rates (CRs), and outage probabilities under different beamforming strategies to investigate fundamental information theoretical limits for HISAC. Further insights are gained by examining high signal-to-noise ratio slopes and diversity orders. A Pareto optimal design is proposed to characterize the attainable SR-CR region. Numerical results reveal that HISAC outperforms the conventional MIMO based ISAC and the HMIMO based frequency-division sensing and communications system in terms of both sensing and communications.

A closed-form expression for the secrecy capacity under near-field communications is derived and compared with its far-field counterpart. To gain further insights, the capacity scaling law is revealed by assuming an infinitely large transmit array and an infinitely high power. Both analytical and numerical results demonstrate that, different from the far-field scenario, i) nearfield communications expand the areas where secure transmission is feasible, specifically when the eavesdropper is located in the same direction as the intended receiver; ii) as the number of transmit antennas increases, the near-field secrecy capacity is capped at a finite value, adhering to the principle of energy conservation.

Recent advancements in large language models (LLMs) have driven a revolutionary paradigm shift in process automation from Robotic Process Automation to Agentic Process Automation by automating the workflow orchestration procedure based on LLMs. However, existing LLMs (even the advanced OpenAI GPT-4o) are confined to achieving satisfactory capability in workflow orchestration. To address this limitation, we present WorkflowLLM, a data-centric framework elaborately designed to enhance the capability of LLMs in workflow orchestration. It first constructs a large-scale fine-tuning dataset WorkflowBench with 106, 763 samples, covering 1, 503 APIs from 83 applications across 28 categories. Specifically, the construction process can be divided into three phases: (1) Data Collection: we collect real-world workflow data from Apple Shortcuts and RoutineHub, transcribing them into Python-style code. We further equip them with generated hierarchical thought via GPT-4o-mini. (2) Query Expansion: we prompt GPT-4o-mini to generate more task queries to enrich the diversity and complexity of workflows. (3) Workflow Generation: we leverage an annotator model trained on collected data to generate workflows for synthesized queries. Finally, we merge the synthetic samples that pass quality confirmation with the collected samples to obtain the WorkflowBench. Based on WorkflowBench, we fine-tune Llama-3.1-8B to obtain WorkflowLlama. Our experiments show that WorkflowLlama demonstrates a strong capacity to orchestrate complex workflows, while also achieving notable generalization performance on previously unseen APIs. Additionally, WorkflowBench exhibits robust zero-shot generalization capabilities on an out-of-distribution task planning dataset, T-Eval. Our data and code are available at https://github.com/OpenBMB/WorkflowLLM.

A Pinching-Antenna SyStem (PASS)-assisted integrated sensing and communications (ISAC) framework is established, where a pinched waveguide is utilized to simultaneously communicate with a user and sense a target. Closed-form expressions for the achievable communication rate (CR) and sensing rate (SR) are derived to characterize the information-theoretic limits of this dual-functional operation. Closed-form solutions for the optimal pinching antenna location are derived under sensing-centric (S-C), communications-centric (C-C), and Pareto-optimal designs. On this basis, the CR-SR trade-off is characterized by deriving the full CR-SR rate region. Numerical results demonstrate that PASS can achieve a larger rate region than conventional fixed-antenna systems.

Low-cost and low-power consumptions have become the trend for the future communication systems, where one-bit quantization is a promising candidate. However, due to the low resolutions, the performance loss of the one-bit system is serious. To alleviate this issue, we propose leveraging the fluid reconfigurable intelligent surface (fRIS) to compensate for the loss in downlink communication systems with one-bit digital-to-analog converters (DACs). Specifically, we formulate a symbol estimation error minimization problem by jointly optimizing the symbol estimator, the one-bit transmit signal, the fRIS phase shifts, and the element positions. To handle the resultant problem, an alternating algorithm is developed, where four subproblems are solved iteratively by using semidefinite-relaxation, discrete optimization, and quadratic programming. In optimizing fRIS element positions, the positions affect both the incident and reflective channels, leading to the high-order complex objective functions. We show that these terms can be simplified by using the characteristics of channels. Numerical results validate that the introduction of fRIS can alleviate the performance loss caused by one-bit quantization.

Achieving higher spectrum efficiency has been a critical performance target for the forthcoming 6G era. Non-orthogonal multiple access (NOMA) is regarded as an effective technique to improve the spectrum efficiency a nd t he cluster-free NOMA is a novel generalized downlink NOMA transmission framework. However, there still lacks an efficient joint beamforming and SIC optimization algorithm for cluster-free NOMA. In this paper, we propose a novel channel correlation based two-loop greedy (CC-TLG) algorithm to obtain a near-optimal solution with dramatically reduced complexity. CC-TLG maximizes the WSR of users based on the user channel correlation coefficients, where the outer loop add users one by one based on the WSR in a greedy manner, and the inner loop generates a near-optimal SIC operation based on both the channel correlation coefficients and WSR in a greedy way. Finally, simulations demonstrates the effectiveness of the proposed CC-TLG algorithm.

This study investigates the application of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided medium-Earth-orbit (MEO) satellite network for providing both global positioning services and communication services in the urban canyons, where the direct satellite-user links are obstructed. Superposition coding (SC) and successive interference cancellation (SIC) techniques are utilized for the integrated navigation and communication (INAC) networks, and the composed navigation and communication signals are reflected or transmitted to ground users or indoor users located in urban canyons. To meet diverse application needs, navigation-oriented (NO)-INAC and communicationoriented (CO)-INAC have been developed, each tailored according to distinct power allocation factors. We then proposed two algorithms, namely navigation-prioritized-algorithm (NPA) and communication-prioritized-algorithm (CPA), to improve the navigation or communication performance by selecting the satellite with the optimized position dilution of precision (PDoP) or with the best channel gain.

The real-time near-field beamforming framework with the aided of 3D velocity sensing and localization for unmanned aerial vehicle (UAV) communications is proposed. Exploiting the variant Doppler shift over the spatial domain in the near field, the three-dimensional (3D) velocities are estimated with the echo signals. To provide timely correction of the location prediction errors with the estimated velocities, the extended Kalman filter (EKF) is adopted to fuse the predicted states and the estimated ones. Subsequently, the near-field beamforming can be conducted with the predicted locations of the UAV, thereby realizing zero-pilot and low-latency transmission. Numerical results unveil that, the proposed scheme can achieve the accurate tracking of the UAV's flying route.

In this paper, a robust transmission design is investigated for an intelligent reflecting surface (IRS)-assisted secure wireless powered communication network (WPCN) in the presence of hardware impairments at the transceiver. An IRS is deployed to enhance the efficiency of downlink (DL) wireless energy transfer (WET) and the security of uplink (UL) wireless information transfer (WIT). To maximize the secrecy throughput, the DL and UL time allocation, the transmit beamforming vector at the power station (PS), the receive beamforming vector at the access point (AP), and phase-shifts of IRS in DL and UL are jointly optimized. To handle the resulting non-convex optimization problem, an efficient algorithm based on block coordinate descent (BCD) is developed to iteratively update the optimization variables. Specifically, the closed-form solution of the receive beamforming vector is derived and the transmit beamforming vector can be obtained by the successive convex approximation (SCA) method. Then, the semidefinite relaxation (SDR) method is used to solve the corresponding IRS phase-shifts optimization sub-problems in DL and UL. Simulation results unveil that simultaneously optimizing the IRS phase-shifts in DL and UL can largely compensate for the hardware impairments compared to the schemes that optimize either DL/UL alone. Furthermore, the proposed robust transmission design scheme achieves higher performance improvement compared to the non-robust design which ignores the impact of hardware impairments.

Terahertz (THz) offers the availability of huge bandwidth to provide unprecedented data rate for the sixth-generation mobile communication and beyond. Since the narrow beam would be transmitted in the terminals to address severe path loss, THz beam alignment has been a source of significant overhead. The Direction of Arrival (DoA) sensed from a low-frequency band communication system could help fast beam alignment of THz communication. However, it is inefficient since it wastes hardware sources and requires exchange information between the sub-6 GHz and THz systems. In this article, based on the spatial similarity of the intermediate frequency (IF) channel and THz channel, a THz communication system is designed by sensing the DoA of the leaky waves from IF ports of the classical superheterodyne communication structure to aid the THz beam alignment, in which the IF part is controlled by a switching network. It is fast and efficient, since the DoA is employed and no additional communication baseband is used. Compared to the conventional beam alignment methods, the proposed shows well performance on accuracy, reactiveness and overhead, especially in scenarios for limited channel measurement as well as massive multiple-input multiple-output which are regular in THz communication.

A novel framework of the uncrewed aerial vehicle (UAV)-mounted active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) communications with the nonorthogonal multiple access (NOMA) is proposed for Internet of Things (IoT) networks. In particular, an active STAR-RIS is deployed onboard to enhance the communication link between the base station (BS) and the IoT devices, and NOMA is utilized for supporting the multidevice connectivity. Based on the proposed framework, a system sum rate maximization problem is formulated for the joint optimization of the active STAR-RIS beamforming, the UAV trajectory design, and the power allocation. To solve the nonconvex problem with highly coupled variables, an alternating optimization (AO) algorithm is proposed to decouple the original problem into three subproblems. Specifically, for the active STAR-RIS beamforming, the amplification coefficient, the power-splitting ratio, and the phase shift are incorporated into a combined variable to simplify the optimization process. Afterward, the penalty-based method is invoked for handling the nonconvex rank-one constraint. For the UAV trajectory design and the power allocation subproblems, the successive convex optimization method is applied for iteratively approximating the local-optimal solution. Numerical results demonstrate that: 1) the proposed algorithm achieves superior performance compared to the benchmarks in terms of the sum rate and 2) the UAV-mounted active STAR-RIS can effectively enhance the channel gain from the BS to the IoT devices by the high-quality channel construction and the power compensation.

Mobile-edge generation (MEG) is an emerging technology that allows the network to meet the challenging traffic load expectations posed by the rise of generative artificial intelligence (GAI). A novel MEG model is proposed for deploying GAI models on edge servers (ESs) and user equipment (UE) to jointly complete text-to-image generation tasks. In the generation task, the ES and UE will cooperatively generate the image according to the text prompt given by the user. To enable the MEG, a pretrained latent diffusion model (LDM) is invoked to generate the latent feature, and an edge-inferencing MEG protocol is employed for data transmission exchange between the ES and the UE. A compression coding technique is proposed for compressing the latent features to produce seeds. Based on the above seed-enabled MEG model, an image quality optimization problem with energy constraint is formulated. The transmitting power of the seed is dynamically optimized by a deep reinforcement learning (DRL) agent over the fading channel. The proposed MEG-enabled text-to-image generation system is evaluated in terms of image quality and transmission overhead. The numerical results indicate that, compared to the conventional centralized generation-and-downloading scheme, the symbol number of the transmission of MEG is materially reduced. In addition, the proposed compression coding approach can improve the quality of generated images under low signal-to-noise ratio (SNR) conditions, and the DRL-enabled dynamic power control further improves the image quality under the energy constraint compared to static transmit power control.

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International audience

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.

Few-shot segmentation (FSS) aims to accurately segment target objects in a query image using only a limited number of annotated support images. Existing approaches typically follow a paradigm that directly leverages category information from the support set to identify target objects in the query. However, these methods often ignore the category information gap between query and support images, leading to suboptimal performance when faced with images containing objects exhibiting significant intra-class diversity. To address this issue, we propose a novel framework that introduces intermediate prototypes to capture both deterministic information from the support images and adaptive knowledge from the query at multiple scales. Our framework, named the K-shot Multi-scale Intermediate Prototype Mining Transformer (KMIPMT), is based on the Transformer architecture and learns intermediate prototypes in an iterative manner, where each KMIPMT layer propagates category information from both K-shot support features and multi-scale query features to intermediate prototypes. This information is then utilized to activate the query feature map. Through repeated iterations, both intermediate prototypes and the query feature are progressively enhanced, and the final refined query feature is used for generating precise segmentation predictions. Despite its simplicity, our method achieves remarkable performance gains on standard benchmarks, including PASCAL-$5^{i}$5i, COCO-$20^{i}$20i, and FSS-1000, setting new state-of-the-art results. Furthermore, we explore several practical and challenging extensions of our method, including 3D point cloud FSS, zero-shot segmentation, weak-label FSS, and cross-domain FSS. These extensions showcase the versatility and effectiveness of our proposed KMIPMT framework across different domains and scenarios.

The near-field spatial correlation for multiple-input multiple-output (MIMO) communications in multi-path fading channels is analyzed. Based on the general non-uniform spherical wave (NUSW) model, an analytical integral-form expression for near-field spatial correlation is derived, which generalizes the conventional uniform plane wave (UPW)-based far-field spatial correlation. Furthermore, by considering the specific von Mises-Fisher distribution of scatterer locations, a simplified closed-form near-field spatial correlation expression is derived. It is rigorously proved that 1) in contrast to the far-field spatial correlation, the near-field spatial correlation no longer exhibits spatial stationary property, and 2) the NUSW-based near-field spatial correlation model depends on the power location spectrum, which encompasses both the angles and distances of the scatterers. Next, the developed near-field spatial correlation can be utilized to derive a closed-form expression for the effective degrees of freedom (EDoF). Additionally, a correlation-based stochastic channel model is constructed for MIMO communications, from which an optimal transmission strategy is devised. Subsequently, the power allocation is optimized to achieve the maximum ergodic spectral efficiency. Numerical results validate 1) the significance of near-field spatial correlation modeling for MIMO communications, 2) near-field MIMO exhibits a higher EDoF than the conventional far-field counterpart, and 3) the constructed correlation-based stochastic channel model facilitates the derivation of an optimal transmission strategy, thereby maximizing ergodic spectral efficiency.

A reconfigurable intelligent surface (RIS) assisted semi-grant-free (SGF) non-orthogonal multiple access (NOMA) system is investigated. Unlike existing works that only focus on short-term resource allocation, we study a long-term power-saving optimization problem under queue stability constraints and utilize Lyapunov stability theory to deal with delay-aware resource allocation. We first transform the long-term problem into a series of per-time-slot problems by exploiting the Lyapunov theory. Then, the objective function is minimized by alternatingly optimizing the power allocation, channel assignment, and RIS reflection coefficients. In particular, the channel assignment subproblem is solved by invoking a many-to-one matching algorithm. The power allocation sub-problem is addressed by the developed fractional programming algorithm. The reflection coefficients design sub-problem is solved by a penalty-based method, which tackles the rank one constraint and optimizes reflection coefficients. The numerical results validate the effectiveness and show that it can achieve queue stability by setting the Lyapunov parameters. It also shows that the proposed RIS-assisted SGF NOMA system outperforms without RIS and random RIS phase-shift baselines.

Simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) technique has recently received considerable attention due to its omni-directional radiation capability. In this paper, motivated by the interference-mitigation-based (IMB) and signal-enhancement-based (SEB) designs, we introduce an innovative STAR-RIS assisted simultaneous-signal-enhancement-and-interference-mitigation (SSEIM) design in non-orthogonal multiple access (NOMA) multiple-input single-output cellular communication networks. Our objective is to maximize the system spectral efficiency (SE) by jointly optimizing the reflection and transmission phase shifts at the STAR-RIS, the precoding matrix of BSs, and the power allocation factors of NOMA users. We propose a low-complexity simultaneous enhancement and mitigation algorithm. Furthermore, by exploiting the manifold optimization technique, we introduce the Riemannian conjugate gradient algorithm to solve the non-convex subproblems with unit modulus constraint. Our analysis reveals that the proposed SSEIM design exceeds the traditional RIS-aided SEB and IMB designs.

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes; 2) fading factors have negligible effects on OP and spectral efficiency; and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users.

As one of the key enabler technologies for future networks, terahertz communications (THzComs) are vulnerable to performance degradation and even outage in mobile and near-field scenarios. The emerging integrated sensing and communications (ISAC) technique paves a way to settle the challenges by estimating user position or channel state to achieve beam-alignment, but fails to content the needs of near-field beam-focusing in mobile scenarios. To this end, a closed-form model for terahertz near-field sensing-assisted mobile communications (TNF-SAMC) is provided. Firstly, the TNF-SAMC system models in temporal/frequency/spatial-division schemes are exhibited. Secondly, a near-field four-dimensional sensing framework characterized by Cramér-Rao Bound is put forward. Thirdly, considering the assistance of sensing, the closed-form expressions of Ergodic Shannon Capacity is derived. Next, the theoretical analysis is proceeded to reveal TNF-SAMC performance features in terms of Division Factor, Joint Cramér-Rao Bound, and Critical Point. Especially the simplified closed-form derivations of Critical Points are given, hinting the minimum resources that should be allocated for sensing in TNF-SAMC systems. Then, numerical results verify the conclusion that temporal-division scheme prefers low mobility scenario, spatial-division scheme performs better in beam alignment case, while frequency-division scheme is most superior in TNF-SAMC systems.

A novel modular extremely large-scale multiple-input-multiple-output integrated sensing and communication system is investigated in this paper. The piecewise-far-field channel model is employed to characterize both communication and sensing channels, capturing the far-field propagation within each subarray and the near-field effects among subarrays due to the small subarray aperture and large inter-subarray spacing. Then, a joint transmit-receive beamforming problem is formulated to optimize communication spectral efficiency while satisfying the sensing signal-to-clutter-plus-noise ratio requirement. To solve this problem, an alternating optimization framework is proposed to iteratively update the transmit beamformer and receive beamformer until convergence. For a fixed receive beamformer, a closed-form optimal analog beamformer is firstly derived by exploiting the near-field propagation characteristics among subarrays, transforming the transmit hybrid beamforming problem into a low-dimensional digital beamforming optimization and substantially reducing the computational complexity. Then, two efficient algorithms are proposed to solve the rank-constrained digital beamforming problem. First, the semi-closed form of the optimal digital beamformer is derived and shown to form a complex Stiefel manifold. Based on this structure, a joint Riemannian-Euclidean gradient descent algorithm is developed for iterative optimization. Second, an semidefinite relaxation-based approach is proposed, where a near-optimal solution is obtained through rank constraint relaxation and randomization. Extensive simulations validate the superiority of the proposed algorithms, revealing that the optimal subarray scale balances spatial multiplexing and beamforming gains based on user distance, while increasing subarray numbers significantly enhances range resolution due to more pronounced spherical wavefronts.

In this paper, we propose a novel reconfigurable intelligent surface (RIS) -assisted non-orthogonal multiple access (NOMA) model, where an unmanned aerial vehicle (UAV) is employed for energy transfer. In this framework, the ground users (GUs) utilize energy from the UAV for information transmission to enable long-duration communication self-sufficiency. A sum-rate maximization problem is proposed, which is decomposed into four sub-problems: phase-shift optimization, power allocation, time allocation, and UAV trajectory optimization. These are solved using the SDR algorithm, CVX toolbox, game theory, and PSO algorithm, respectively. Subsequently, a joint resource allocation algorithm based on the block coordinate descent (BCD) method is introduced. Simulation results show that the UAV-assisted RIS-NOMA scheme, along with the proposed BCD algorithm, can increase the system capacity by 48.6% compared to the TDMA scheme as well as the alternating direction multiplier method (ADMM) and whale optimization algorithm (WOA).

In this paper, a novel device-to-device (D2D)-assisted cooperative non-orthogonal multiple access (NOMA) model with a two-stage transmission scenario is proposed, which consists of 1) partial decoding and forwarding from the transmitter to relay nodes; 2) transmission from relay nodes to the receivers. A sum-rate maximization problem is formulated, which is decoupled into subchannel selection and two-stage channel link power allocation. A joint optimization algorithm based on game theory and successive convex approximation (JOAGS) is proposed, which can efficiently utilize network resources and increase spectrum efficiency. The algorithm proposed in this paper has been validated through simulation results, demonstrating its substantial capability to amplify system capacity, diminish the outage probability of the communication link, and extend the communication distance. The findings reveal that when compared to the existing scheme, the system’s sum-rate is augmented by 10.8%, and the outage probability registers a notable reduction of 23.6%.

Distinguishing the near-field and far-field regions in line-of-sight (LoS) multiple-input multiple-output (MIMO) systems is crucial, as their distinct characteristics significantly impact performance and system design. In this paper, we propose a novel criterion for identifying the near-field and far-field regions based on the number of independent spatial streams. We introduce and derive a closed-form expression of effective degree of freedom (EDoF) for LoS MIMO systems by taking into account both the phase differences and path attenuation. Then, the spatial multiplexing distance (SMD) and resolvable distance (RD) are derived to define the boundary of the near field. Based on these boundaries, we propose a hybrid search-gradient descent (HSGD) algorithm to estimate the near-field channel information, which combines a coarse search through non-uniform step sizes with a precise estimation based on the gradient descent. Our numerical results unveil that i) the EDoF can be accurately calculated using the closed-form expression, ii) the HSGD algorithm achieves at least 28.72% improvement over the polar-domain simultaneous iterative gridless weighted (PSIGW) algorithm and 22.91% improvement over the orthogonal matching pursuit (OMP) algorithm across the different signal-to-noise ratio (SNR), and iii) the HSGD algorithm achieves at least 95.84% improvement over the PSIGW algorithm and 79.83% improvement over the OMP algorithm across varying communication distances.

This paper investigates covert communications in an integrated sensing and communications system, where a dual-functional base station (called Alice) covertly transmits signals to a covert user (called Bob) while sensing multiple targets, with one of them acting as a potential watcher (called Willie) and maliciously eavesdropping on legitimate communications. To shelter the covert communications, Alice transmits additional dual-functional artificial noise (DFAN) with a varying power not only to create uncertainty at Willie’s signal reception to confuse Willie but also to sense the targets simultaneously. Based on this framework, the weighted sum of the sensing beampattern means square error (MSE) and cross correlation is minimized by jointly optimizing the covert communications and DFAN signals subject to the minimum covert rate requirement. The robust design considers both cases of imperfect Willie’s CSI (WCSI) and statistical WCSI. Under the worst-case assumption that Willie can adaptively adjust the detection threshold to achieve the best detection performance, the minimum detection error probability (DEP) at Willie is analytically derived in the closed-form expression. The formulated covertness constrained optimization problems are tackled by a feasibility-checking based difference-of-convex relaxation (DC) algorithm utilizing the S-procedure, Bernstein-type inequality, and the DC method. Simulation results validate the feasibility of the proposed scheme and demonstrate the covertness performance gains achieved by our proposed design over various benchmarks.

A near-field wideband communication system is investigated in which a base station (BS) employs an extra-large scale antenna array (ELAA) to serve multiple users in its near-field region. To facilitate near-field multi-user beamforming and mitigate the spatial wideband effect, the BS employs a hybrid beamforming architecture based on true-time delayers (TTDs). In addition to the conventional fully-connected TTD-based hybrid beamforming architecture, a new sub-connected architecture is proposed to improve energy efficiency and reduce hardware requirements. Two wideband beamforming optimization approaches are proposed to maximize spectral efficiency for both architectures. 1) Fully-digital approximation (FDA) approach: In this method, the TTD-based hybrid beamformer is optimized by the block-coordinate descent and penalty method to approximate the optimal digital beamformer. This approach ensures convergence to the stationary point of the spectral efficiency maximization problem. 2) Heuristic two-stage (HTS) approach: In this approach, the analog and digital beamformers are designed in two stages. In particular, two low-complexity methods are proposed to design the high-dimensional analog beamformers based on approximate and exact line-of-sight channels, respectively. Subsequently, the low-dimensional digital beamformer is optimized based on the low-dimensional equivalent channels, resulting in reduced computational complexity and channel estimation complexity. Our numerical results show that 1) the proposed approach effectively eliminates the spatial wideband effect, and 2) the proposed sub-connected architecture is more energy efficient and has fewer hardware constraints on the TTD and system bandwidth compared to the fully-connected architecture.

The optimal beamforming design for multi-user continuous aperture array (CAPA) systems is proposed. In contrast to conventional spatially discrete array (SPDA), the beamformer for CAPA is a continuous function rather than a discrete vector or matrix, rendering beamforming optimization a non-convex integral-based functional programming. To address this challenging issue, the closed-form optimal structure of the CAPA beamformer is first derived for maximizing generic system utility functions, by addressing the inversion of continuous functions and using the Lagrangian duality and the calculus of variations. The derived optimal structure is a linear combination of the continuous channel responses for CAPA, with the linear weights determined by the channel correlations. As a further advance, a monotonic optimization method is proposed for obtaining globally optimal CAPA beamforming based on the derived optimal structure. More particularly, a closed-form fixed-point iteration is proposed to obtain the globally optimal solution to the power minimization problem for CAPA beamforming. Furthermore, based on the optimal structure, the low-complexity maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean-squared error (MMSE) designs for CAPA beamforming are derived. It is theoretically proved that: 1) the MRT and ZF designs are asymptotically optimal in low and high signal-to-noise ratio (SNR) regimes, respectively, and 2) the MMSE design is optimal for signal-to-leakage-plus-noise ratio (SLNR) maximization. Our numerical results validate the effectiveness of the proposed designs and reveal that:i)CAPA achieves significant communication performance gain over SPDA, andii)the MMSE design achieves nearly optimal performance in most cases, while the MRT and ZF designs achieve nearly optimal performance in specific cases.

The Pinching-Antenna SyStem (PASS) is a revolutionary flexible antenna technology designed to enhance wireless communication by establishing strong line-of-sight (LoS) links, reducing free-space path loss and enabling antenna array reconfigurability. PASS uses dielectric waveguides with low propagation loss for signal transmission, radiating via a passive pinching antenna, which is a small dielectric element applied to the waveguide. This paper first proposes a physics-based hardware model for PASS, where the pinching antenna is modeled as an open-ended directional coupler, and the electromagnetic field behavior is analyzed using coupled-mode theory. A simplified signal model characterizes the coupling effect between multiple antennas on the same waveguide. Based on this, two power models are proposed: equal power and proportional power models. Additionally, a transmit power minimization problem is formulated/studied for the joint optimization of transmit and pinching beamforming under both continuous and discrete pinching antenna activations. Two algorithms are proposed to solve this multimodal optimization problem: the penalty-based alternating optimization algorithm and a low-complexity zero-forcing (ZF)-based algorithm. Numerical results show that 1) the ZF-based low-complexity algorithm performs similarly to the penalty-based algorithm, 2) PASS reduces transmit power by over 95% compared to conventional and massive MIMO, 3) discrete activation causes minimal performance loss but requires a dense antenna set to match continuous activation, and 4) the proportional power model yields performance comparable to the equal power model.

Satellite communication is a crucial component for achieving global communications in sixth generation (6G). Due to the large distance between the satellite and the terrestrial users, the multiplicative fading effects of traditional passive simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are more severe in satellite communication. Recently, active simultaneous transmitting and reflecting surfaces (ASTARSs) have been proposed to mitigate multiplicative fading by amplifying the incident signal. Motivated by the above, a novel ASTARS-aided non-orthogonal multiple access (NOMA) satellite communication network is proposed in this paper, which includes one low earth orbit (LEO) satellite, one ASTARS and several far-field users (FFUs) and near-field users (NFUs). We propose an adaptive NOMA pairing strategy that allows the satellite to flexibly select pairing schemes. Depending on the users’ quality of service requirements, either the NFU-FFU (NF) pairing scheme or the NFU-NFU (NN) pairing scheme can be used. Then, we formulate an optimization problem to maximize the channel capacity. A two-layer optimization algorithm is proposed, where the outer layer selects the NOMA user pairing schemes, while the inner layer alternately optimizes the NOMA power allocation factors, the satellite precoding matrix and the ASTARS phase shift matrices. To solve the non-convex optimization problem, successive convex approximation and a penalty-based method are employed. The numerical results reveal: 1) The channel capacity of the NF pairing scheme is always higher than that of the NN pairing scheme; 2) Compared to the “Without RIS" and “Passive STARS" schemes, ASTARS can significantly improve the channel capacity of satellite communication.

A novel hard-latency guaranteed cluster-free multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) framework is proposed to deal with burst traffics that commonly occur in real-world scenarios. The hard-latency constrained effective throughput (HLC-ET) maximization problem is formulated, which jointly optimizes the beamforming and cluster-free success interference cancellation (SIC) operations. To address the resultant problem, a two-stage reinforcement learning (RL)-based algorithm is developed to capture system uncertainty, where the large-dimension optimization is decoupled into two stages to reduce the action space and fasten convergence of RL. In the long-term stage, we aim to maximize the HLC-ET, and a hybrid RL algorithm with policy reuse is adoped to control the priority weights to construct the weighted sum rate (WSR) function of users. In the short-term stage, a branch-and-bound (BB) based algorithm is further developed to obtain the optimal solution of the WSR maximization problem. The BB-based algorithm is proved to guarantee the convergence to an ϵ-optimal solution of the WSR maximization problem within a finite number of steps. To accelerate computation in the short-term stage, a channel correlation based two-loop greedy (CC-TLG) algorithm is proposed to significantly reduce the complexity with almost no performance loss compared to the BB-based algorithm. Finally, simulations demonstrate the advantages of the proposed two-stage RL based joint beamforming and SIC optimization (TSRL-JBSO) algorithm over conventional RL-based and non-RL based algorithms.

A novel beam tracking approach is proposed to realize reliable air-to-ground (A2G) transmissions with reduced pilot overhead and time delay. The proposed beam tracking strategy consists of two stages, namely the model-driven channel tracking and the model-data dual driven hybrid beamforming (HBF). For the model-driven channel tracking, the angle-of-arrivals/angle-of-departures (AoAs/AoDs) are predicted by leveraging the regularity of the three-dimensional flight track and attitude, as well as the A2G geometrical information with temporal correlations. Then, the high-dimensional channel matrix estimation problem is converted to the low-dimensional multipath components parameters estimation tasks, which substantially reduces the pilot overhead. The proposed model-data dual-driven HBF module unfolds the iterative HBF algorithms and introduces a set of trainable parameters, which brings in both low complexity and high interpretability. To further improve the HBF robustness against imperfect channel state information, the denoise neural network is employed to exploit spatial-domain channel correlations for improved channel accuracy. Numerical results unveil that: 1) the proposed model-driven channel tracking scheme achieves satisfying normalized mean square error of the tracked A2G channel with significantly reduced pilot overhead; and 2) the proposed model-data dual-driven HBF algorithm is superior to the conventional counterparts in terms of reliability and robustness.

A holographic multiple-input multiple-output (MIMO)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. The spatial correlation is incorporated into the communication channel modeling, while a spherical wave-based model is used to characterize the sensing link. By considering both instantaneous and statistical channel state information, closed-form expressions are derived for sensing rates (SRs), communication rates (CRs), and outage probabilities under various ISAC designs. This enables an investigation into the theoretical performance limits of the proposed holographic MIMO-based ISAC (HISAC) framework. Further insights are gained by examining the high signal-to-noise ratio (SNR) slopes and diversity orders. Specifically: I) for the downlink case, a sensing-centric (S-C) design and a communications-centric (C-C) design are investigated using different beamforming strategies, and a Pareto optimal design is proposed to characterize the attainable SR-CR region; II) for the uplink case, the S-C design and the C-C design differ in the interference cancellation order between the communication and sensing signals, with the rate region obtained through a time-sharing strategy. Numerical results are provided to demonstrate that HISAC systems outperform both conventional MIMO-based ISAC systems and holographic MIMO-based frequency-division sensing and communications systems, underscoring the superior performance of the HISAC framework.

A continuous aperture array (CAPA)-based secure communication system is investigated, where a base station (BS) equipped with a CAPA transmits signals to a legitimate user under the existence of an eavesdropper. For improving the secrecy performance, the artificial noise (AN) is employed at the BS for the jamming purpose. We aim at maximizing the secrecy rate by jointly optimizing the information-bearing and AN source current patterns, subject to the maximum transmit power constraint. To solve the resultant non-convex integral-based functional programming problem, a channel subspace-based approach is first proposed via exploiting the result that the optimal current patterns always lie within the subspace spanned by all users’ channel responses. Then, the intractable CAPA continuous source current pattern design problem with an infinite number of optimization variables is equivalently transformed into the channel-subspace weighting factor optimization problem with a finite number of optimization variables. A penalty-based successive convex approximation method is developed for iteratively optimizing the finite-size weighting vectors. To further reduce the computational complexity, we propose a two-stage source current patterns design scheme. Specifically, the information-bearing and AN patterns are first designed using the maximal ration transmission (MRT) and zero-forcing (ZF) transmission, respectively. Then, the remaining power allocation is addressed via the one-dimensional search method. Numerical results unveil that 1) the CAPA brings in significant secrecy rate gain compared to the conventional discrete multiple-input multiple-output (MIMO); 2) the proposed channel subspace-based algorithm outperforms the conventional Fourier-based approach, while sustaining much lower computational complexity; and 3) the two-stage ZF-MRT approach has negligible performance loss for the large transmit power regime.

Next-generation mobile communication systems will employ higher frequency bands and larger antenna arrays to meet the growing demand for data rates. However, this shift will extend the Rayleigh distance, resulting in near-field effects. Traditional far-field algorithms for source sensing yield considerable errors under these conditions. Near-field spatial spectrum estimation algorithms require multi-dimensional spectral peak searches, which lead to high computational complexity and necessitate a trade-off between sensing accuracy and real-time performance. Consequently, there is an urgent need for high-precision, low-complexity algorithms suitable for near-field sensing. This study proposes a calibration algorithm for spectral peak searches in near-field spatial spectra, referred to as the joint parabolic interpolation and barycenter calibration (PI-BC) algorithm. Simulation results indicate that, compared to existing parameter estimation algorithms, the joint PI-BC algorithm significantly improves sensing accuracy. Furthermore, the computational complexity of the calibration process in the joint PI-BC algorithm is negligibly low.

Few-shot semantic segmentation (FSS) aims to segment the target object under the condition of a few annotated samples. However, current studies on FSS primarily concentrate on extracting information related to the object, resulting in inadequate identification of ambiguous regions, particularly in non-target areas, including the background (BG) and Distracting Objects (DOs). Intuitively, to alleviate this problem, we propose a novel framework, namely NTRENet++, to explicitly mine and eliminate BG and DO regions in the query. First, we introduce a BG Mining Module (BGMM) to extract BG information and generate a comprehensive BG prototype from all images. For this purpose, a BG mining loss is formulated to supervise the learning of BGMM, utilizing only the known target object segmentation ground truth. Subsequently, based on this BG prototype, we employ a BG Eliminating Module to filter out the BG information from the query and obtain a BG-free result. Following this, the target information is utilized in the target matching module to generate the initial segmentation result. Finally, a DO Eliminating Module is proposed to further mine and eliminate DO regions, based on which we can obtain a BG and DO-free target object segmentation result. Moreover, we present a prototypical-pixel contrastive learning algorithm to enhance the model’s capability to differentiate the target object from DOs. Extensive experiments conducted on both PASCAL-5i and COCO-20i datasets demonstrate the effectiveness of our approach despite its simplicity. Additionally, we extend our method to the few-shot video object segmentation task and achieve improved performance on a baseline model, demonstrating its generalization ability. Code is available athttps://github.com/LIUYUANWEI98/NTRENet++.

Cellular-connected uncrewed aerial vehicles (UAVs) have gained increasing attention due to the potential to leverage existing cellular infrastructure for reliable communications between UAVs and base stations. They have been used for various applications, including weather forecasting and search and rescue operations. However, under extreme weather conditions such as rainfall, the trajectory design of cellular UAVs is quite challenging due to weak coverage regions in the sky, limitations of UAV flying time, and signal attenuation caused by raindrops. To this end, this paper proposes a physics-based trajectory design approach for cellular-connected UAVs in rainy environments. A physics-based electromagnetic simulator is utilized to take into account detailed environment information and the impact of rain on radio wave propagation. The trajectory optimization problem is formulated to jointly consider UAV flying time and signal-to-interference ratio, and is solved through a Markov decision process using deep reinforcement learning algorithms based on multi-step learning and double Q-learning. Optimal UAV trajectories are compared in examples with homogeneous atmosphere medium and rain medium. Additionally, a thorough study of varying weather conditions on trajectory design is provided, and the impact of weight coefficients in the UAV trajectory design is discussed. The proposed approach has demonstrated great potential for UAV trajectory design under rainy weather conditions.

Driven by diverse applications, radio access networks (RAN) are expected to embrace built-in computing and intelligence, forming a versatile wireless computing platform that closely integrates communication and computing. To fully unleash the potential of such a synergistic system, it is essential to coordinate communication and computing with intelligence unlocked by the radio intelligent controllers (RICs) in O-RAN. Building on the groundwork established by existing theoretical studies and simulations, we develop a platform that can emulate the events in the real-world system in more detail, bringing theoretical works closer to practical implementation. In this paper, we first introducens-GP-O-RAN, a software simulation platform developed over ns-3, enabling communication, computation task processing, large-scale data collection, and testing of system-level orchestration policies through user-level control. Taking virtual reality (VR) as an example, we formulate the computation offloading problem and develop a prediction-based computation offloading xAPP, which contains a prediction phase to predict users’ end-to-end (E2E) performance with the deep neural network and a system-level decision-making phase for global orchestration with the differential evolution algorithm. We evaluate the system capacity and E2E latency over the developed ns-GP-O-RAN, which is more effective than existing approaches.

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can provide a fullcoverage agile radio environment. A unique STAR-RIS-assisted coordinated multi-point (CoMP) framework is investigated in this paper, where cell-center and cell-edge users are embellished by the reflection and transmission features of the STAR-RIS. Unlike previous works controlling the transmission and reflection phase-shift independently, we consider a more practical coupled phase-shift model. We formulate an online active and passive beamforming problem to maximize long-term energy efficiency (EE) with time-varying locations and channels. Moreover, we propose a hybrid learning framework combining model-free and model-based optimization techniques. For the model-free method, we invoke a risk-sensitive multi-agent deep reinforcement learning algorithm to accelerate the online optimization of the passive beamforming of all STAR-RISs. For the model-based method, we invoke fractional programming (FP) to optimize the coordinated zero-forcing beamformer among all base stations and to realize an exact reward evaluation for each action in the DRL algorithm. Compared to DRL algorithms that optimize passive and active beamforming together, we dramatically shrink the action and state spaces. Comprehensive numerical results explain that the STAR-RIS enhanced CoMP system accomplishes a more excellent EE than the benchmark STAR-RIS cases and that without STAR-RIS.

The forthcoming sixth generation (6G) mobile communication system aims to advance technologies that span and integrate computation and communications. Computing power networks (CPNs) and virtualized radio access networks (vRANs) are regarded as two fundamental techniques to achieve this integration. Network functions of virtualized next-generation Node Bs (vgNBs) are implemented on general-purpose servers to process protocol stacks. The energy consumption of vgNBs accounts for a significant portion of energy consumption. However, the proliferation of computing power nodes results in increased energy consumption in CPNs. Power usage effectiveness (PUE) reflects the efficiency of computing nodes while efficiency of computing power (ECP) is adopted to indicate data rates per computing power unit. In this work, a joint load adjustment and sleep management scheme was designed to maximize ECP while minimizing PUE. The optimization problem was formulated as a mixed integer non-linear programming (MINLP) problem, which is NP-hard. A quantum genetic algorithm (QGA) with non-equal size quantum register was suggested to solve this problem. Simulation results demonstrated that the proposed algorithm could outperform benchmark approaches in terms of convergence speed, ECP, PUE, and computing power consumption. When compared to other methods, the proposed approach could improve ECP and computation energy consumption by up to 19.5% and 21.7%, respectively.

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations.

In this paper, we redesign the hybrid analog/digital precoding scheme to form focused beams at specific spatial locations for near-fieldintegrated sensing and communications(ISAC) systems. Specifically, we derive the near-fieldCramér-Rao bound(CRB) for joint distance and angle sensing with arbitrary signal coherence matrices. Unlike previous optimization strategies that either maximize the communication rate or maximize the sensing performance, we aim to maximize the achievable communication rate under unit sensing error. The optimization problem is modeled to maximize the ratio of the sum rate to the CRB by jointly optimizing the analog and digital precoders. Since it is difficult to solve the problem with complicated non-convex fractional program, we first perform an equivalent reformulation to remove the fractional constraint. Then, the fully-digital precoder is obtained bysuccessive convex approximation(SCA). Finally, a low-complexity hybrid precoding algorithm based on alternate optimization is proposed to divide the fullydigital precoder into analog and digital precoders. Simulation results show the effectiveness of the proposed algorithm and the performance of the proposed beam focusing is superior to beam steering in the near-field.

A general fading model for multipath channels between two non-parallel continuous-aperture arrays (CAPAs) is proposed. Building on this model, the performance of diversity and multiplexing achieved by CAPAs over fading channels is analyzed. i) For multiple-input single-output (MISO) and singleinput multiple-output (SIMO) channels, Landau’s eigenvalue theorem is applied to analyze the autocorrelation of the spatial response. Closed-form expressions are derived for the outage probability (OP) and ergodic channel capacity (ECC). Asymptotic analyses in the high signal-to-noise ratio (SNR) regime are conducted to reveal the maximal achievable diversity and multiplexing gains. The diversity-multiplexing trade-off (DMT) is characterized, along with the array gain within the DMT framework. ii) For multiple-input multiple-output (MIMO) channels, a wavenumber-domain-based transmission framework is proposed to leverage the spatial degrees of freedom offered by CAPAs. Asymptotic approximations for the OP and ECC are derived, and the DMT is explored. The performance of CAPAs is further compared with that of conventional spatially-discrete arrays (SPDAs). Analytical and numerical results demonstrate that: i) CAPAs achieve a lower OP and higher ECC than SPDAs; ii) CAPAs achieve the same DMT as SPDAs with antenna spacing no larger than half a wavelength while attaining a higher array gain; and iii) CAPAs outperform SPDAs with antenna spacing greater than half a wavelength in terms of DMT.

In this paper, we employ massive multiple-input and multiple-output (MIMO) technology to support multiple Internet-of-Things devices with ultra-reliability and low-latency communication (URLLC) industrial applications. Specifically, we first derive lower bounds (LBs) on the achievable uplink (UL) and downlink (DL) data rates under the finite blocklength (FBL) and pilot contamination, where each base station (BS) employs maximum-ratio transmission (MRT) in the DL and maximum-ratio combining (MRC) in the UL detection. In addition, the LB rates are derived for two types of pilot of the regular pilot (RP) and superimposed pilot (SP). We study joint UL-DL power allocation optimization where the objective is to maximize the UL-DL overall average weighted sum rate (WSR) for the systems individually with RP and SP schemes. We propose to employ successive convex approximation to transform the original problems into a series of geometric program problems. Then, an iterative algorithm is proposed to jointly optimize the UL and DL pilot and data payload power allocation. Simulation results are shown to compare the performances of the systems with RP and SP schemes for different settings. Simulation results also verify that the derived LB rates tightly match the corresponding ergodic rates and confirm the rapid convergence speed of the proposed iterative algorithms.

True-time delayers (TTDs) are popular analog devices for facilitating near-field wideband beamforming subject to the spatial-wideband effect. In this paper, an adaptive TTD configuration is proposed for short-range TTDs. Compared to the existing TTD configurations, the proposed one can effectively combat the spatial-wideband effect for arbitrary user locations and array shapes with the aid of a switch network. A novel end-to-end deep neural network is proposed to optimize the hybrid beamforming with adaptive TTDs for maximizing spectral efficiency. First, based on the U-Net architecture, a near-field channel learning module (NFC-LM) is proposed for adaptive beamformer design through extracting the latent channel response features of various users across different frequencies. In the NFC-LM, an improved cross attention (CA) is introduced to further optimize beamformer design by enhancing the latent feature connection between near-field channel and different beamformers. Second, a switch multi-user transformer (S-MT) is proposed to adaptively control the connection between TTDs and phase shifters (PSs). In the S-MT, an improved multi-head attention, namely multi-user attention (MSA), is introduced to optimize the switch network by exploring the latent channel relations among various users. Third, a multi-feature cross attention (MCA) is introduced to simultaneously optimize the NFC-LM and S-MT by enhancing the latent feature correlation between beamformers and the switch network. Numerical simulation results show that 1) the proposed adaptive TTD configuration effectively eliminates the spatial-wideband effect under uniform linear array (ULA) and uniform circular array (UCA) architectures, and 2) the proposed deep neural network can provide near-optimal spectral efficiency, and solve the multi-user beamformer design and dynamical connection problem in real-time.

The performance of near-field sensing (NISE) in a legacy wideband multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system is analyzed. The maximum likelihood estimates (MLE) for the target’s distance and angle relative to the antenna array are derived. To evaluate the estimation error, closedform analytical expressions of Cram´er-Rao bounds (CRBs) are derived for both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). The asymptotic CRBs are then analyzed to reveal the scaling laws of CRBs with respect to key system parameters, including array size, bandwidth, and target distance. Our results reveal that 1) the mean-squared error achieved by MLEs approaches CRBs in the high signal-to-noise ratio regime; 2) a larger array aperture does not necessarily improve NISE performance, especially with ultra-large bandwidth; 3) large bandwidth sets an estimation error ceiling for NISE as target distance increases; 4) array aperture and bandwidth, rather than the number of antennas and subcarriers, are the key factors affecting wideband NISE performance; and 5) UCAs offer superior, angle-independent wideband NISE performance compared to ULAs with the same aperture.

The beamforming optimization in continuous aperture array (CAPA)-based multi-user communications is studied. In contrast to conventional spatially discrete antenna arrays, CAPAs can exploit the full spatial degrees of freedom (DoFs) by emitting information-bearing electromagnetic (EM) waves through continuous source current distributed across the aperture. Nevertheless, such an operation renders the beamforming optimization problem as a non-convex integral-based functional programming problem, which is challenging for conventional discrete optimization methods. A couple of low-complexity approaches are proposed to solve the functional programming problem. 1) Calculus of variations (CoV)-based approach: Closed-form structure of the optimal continuous source patterns are derived based on CoV, inspiring a low-complexity integral-free iterative algorithm for solving the functional programming problem. 2) Correlation-based zero-forcing (Corr-ZF) approach: Closed-form ZF source current patterns that completely eliminate the inter-user interference are derived based on the channel correlations. By using these patterns, the original functional programming problem is transformed to a simple power allocation problem, which can be solved using the classical water-filling approach with reduced complexity. Our numerical results validate the effectiveness of the proposed designs and reveal that: 1) compared to the state-of-the-art Fourier-based discretization approach, the proposed CoV-based approach not only improves communication performance but also reduces computational complexity by up to hundreds of times for large CAPA apertures and high frequencies, and 2) the proposed Corr-ZF approach achieves asymptotically optimal performance compared to the CoV-based approach.

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramér-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput; 2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios; and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced.

A hybrid non-orthogonal multiple access (HNOMA) empowered integrated sensing and communications (ISAC) framework is proposed, which adaptively manages the additional sensing-to-communication (S2C) interference to save the transmit power. Two scenarios with different numbers of communication users (CUs) are investigated. For the first scenario where the number of CUs does not exceed the number of transmit antennas, a mixed integer problem is formulated to optimize the beamforming (BF) design and successive interference cancellation (SIC) options. An ideal case is primarily inspected, which unveils an insight into the required number of dedicated sensing beams. Inspired by this insight, the SIC options are determined while the remaining BF design is solved via semidefinite relaxation (SDR). For the second scenario where the number of CUs exceeds the number of transmit antennas, the CUs are further grouped into NOMA clusters to mitigate the communication-to-communication interference. An alternating optimization-based algorithm is developed, where the BF design, SIC options and power allocation are alternatively optimized. Simulation results reveal that: 1) the proposed algorithm achieves power-saving gain compared to the conventional ISAC; 2) the proposed algorithm can further exploit the benefits of NOMA to save transmission power while maintaining the least beampattern mismatch in the second scenario.

A continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) framework is proposed in this paper, where CAPA transceivers are optimized to enhance both target sensing and user communication performance. Novel expressions for achievable communication and sensing rates are derived and CAPA-oriented beamforming is designed to balance the dual-functional Pareto-optimal tradeoff in two scenarios: i) For the single-user single-target case, closed-form continuous beamformers are derived based on communication-, sensing-, and Pareto-optimal criteria to reveal the interrelation of the ISAC rate region with the antenna aperture and channel gains; ii) For the multi-user multi-target case, a general CAPA-ISAC beamforming design algorithm is developed to achieve the Pareto optimality. Beamformer design in the continuous spatial domain is transformed into weight design in the discrete wavenumber domain using Fourier series expansions. Furthermore, alternating optimization, successive convex approximation, and difference of convex techniques are employed to tackle the coupling and non-convexity issues. Numerical results demonstrate that: i) The proposed CAPA-ISAC framework significantly improves both sensing and communication performance and expands the ISAC Pareto rate region; ii) CAPAs exhibit superior beamforming capabilities and reach the ultimate performance limits of spatially discrete arrays (SPDAs).

Integrated sensing and communication (ISAC) enables the next-generation network to possess networked sensing capability, propelling the proliferation of various intelligent applications but introducing complex sensing and communication interference. To this end, this paper studies the cooperative transceiver beamforming design for a multi-cell anti-unmanned aerial vehicle (UAV) ISAC system, where multiple base stations (BSs) collaboratively perform joint UAV sensing. Specifically, to ensure reliable detection, we jointly optimize the ISAC transmit and receive beamformers at BSs and downlink users via maximizing the signal-to-clutter-plus-noise ratio of sensing, taking into account the communication requirements and power constraints. To handle the nonconvex fractional problem, we first propose a centralized beamforming algorithm resorting to alternating optimization, successive convex approximation, and Dinkelbach methods. Then, to alleviate heavy backhaul overhead, a distributed algorithm is put forward, adopting the primal decomposition technique to decouple the inter-cell interference. Numerical results verify that: i) Compared with the standalone sensing by a single BS, the proposed cooperative beamforming design achieves notable enhancement in sensing performance; ii) The designed transceiver beamforming is constructive for interference and clutter suppression in multi-cell ISAC systems.

As a typical instance of the metasurface, intelligent omni-surfaces (IOSs) emerge as a potential technique for coverage extension benefiting from the symmetric reflection and refraction. For large-scale IOSs, the enlarged near-field region allows users to be randomly distributed in both the near and far fields of IOSs. To perform beamforming in such anear-far (NF) field communicationsystem, in this paper, we propose an NF-field codebook design and beam training scheme, which avoids the high complexity of accurate channel state information (CSI) acquisition. We reveal that the traditional Rayleigh distance based NF-field boundary may cause redundant training overhead. Thus, an effective NF-field boundary is introduced in terms of the beamforming gain. We then utilize this NF-field boundary and the symmetric characteristic of the IOS reflective-refractive signals to design an IOS-tailored codebook consisting of multiple codewords covering both the near and far fields. On this basis, a joint reflective-refractive beam training mechanism for IOS-aided systems is presented, where beam training is simultaneously performed in the symmetric regions of the IOS, thereby reducing training overhead. Simulation results show that the proposed scheme achieves a higher sum rate than the traditional codebooks given the same number of codewords, and performs close to the perfect CSI case.

The novel concept of mobile edge generation (MEG) is investigated, where the generative artificial intelligence (GAI) model is partitioned into sub-models to be distributed in the network edge, thus enabling latent feature exchange between the edge server and user equipments (UEs). A seed coding module is introduced to encode the intermediate latent features generated by the GAI sub-model at the edge server into flexibly-sized seed for transmission to UEs, instead of transmitting large-size raw data. A weighted energy consumption minimization problem is formulated by jointly optimizing the seed coding ratio (SCR), transmit power, and computing frequencies while guaranteeing the quality-of-generation requirements including total latency and peak signal-to-noise ratio (PSNR). To enhance the resilience of the MEG models against the channel noise, a joint fine-tuning scheme based on low-rank adaption is proposed to train the introduced rank-reduced bypass matrices and seed coding module. Based on the fine-tuned results, a PSNR model regarding SCR and communication signal-to-noise ratio is established to overcome the optimization difficulty due to the lack of the explicit PSNR model. A proximal policy optimization-based MEG energy consumption optimization (MEG-ECO) algorithm is proposed to solve the formulated problem, where the order of magnitude balancing on state and penalty shaping are exploited for more efficient learning. Numerical results reveal that 1) the fine-tuned MEG models have superior resilience against the channel noise; 2) the proposed MEG-ECO algorithm can significantly reduce energy consumption by up to 87.4% compared to conventional centralized generation and up to 33.5% against MEG without seed coding module; and 3) the energy consumption decreases when more partial models are assigned to the edge server, whereas this impact diminishes as the latency threshold is relaxed.

The channel capacity of near-field (NF) communications is characterized by considering three types of line-of-sight multiuser channels: I) multiple access channel (MAC), II) broadcast channel (BC), and III) multicast channel (MC). For NF MAC and BC, closed-form expressions are derived for the sum-rate capacity as well as the capacity region under a two-user scenario. These results are further extended to scenarios with an arbitrary number of users. For NF MC, closed-form expressions are derived for the two-user channel capacity and the capacity upper bound with more users. Further insights are gleaned by exploring special cases, including scenarios with infinitely large array apertures, co-directional users, and linear arrays. For comparison, the MAC and BC sum-rates achieved by typical linear combiners and precoders are also analyzed. Theoretical and numerical results are presented and compared with far-field communications to demonstrate that: I) the NF capacity of these three channels converges to finite values rather than growing unboundedly as the number of array elements increases; II) the capacity of the MAC and BC with co-directional users can be improved by using the additional range dimensions in NF channels to reduce inter-user interference (IUI); and III) the MC capacity benefits less from the NF effect compared to the MAC and BC, as multicasting is less sensitive to IUI.

The secrecy performance in both near-field and far-field communications is analyzed using two fundamental metrics: the secrecy capacity under a power constraint and the minimum power requirement to achieve a specified secrecy rate target. 1) For the secrecy capacity, a closed-form expression is derived under a discrete-time memoryless setup. This expression is further analyzed under several far-field and near-field channel models, and the capacity scaling law is revealed by assuming an infinitely large transmit array and an infinitely high power. A novel concept of “depth of insecurity” is proposed to evaluate the secrecy performance achieved by near-field beamfocusing. It is demonstrated that increasing the number of transmit antennas reduces this depth and thus improves the secrecy performance. 2) Regarding the minimum required power, a closed-form expression is derived and analyzed within far-field and near-field scenarios. Asymptotic analyses are performed by setting the number of transmit antennas to infinity to unveil the power scaling law. Numerical results are provided to demonstrate that: i) compared to far-field communications, near-field communications expand the areas where secure transmission is feasible, specifically when the eavesdropper is located in the same direction as the intended receiver; ii) as the number of transmit antennas increases, neither the secrecy capacity nor the minimum required power scales or vanishes unboundedly, adhering to the principle of energy conservation.

The capacity limits of continuous-aperture array (CAPA)-based wireless communications are characterized. To this end, an analytically tractable transmission framework is established for both uplink and downlink CAPA systems. Based on this framework, closed-form expressions for the single-user channel capacity are derived. The results are further extended to a multiuser case by characterizing the capacity limits of a two-user channel and proposing the associated capacity-achieving decoding and encoding schemes. In the uplink case, the capacity-achieving detectors and sum-rate capacity are derived, and the capacity region is characterized. In the downlink case, the uplink-downlink duality is established by deriving the uplink-to-downlink and downlink-to-uplink transformations under the same power constraint, based on which the optimal source current distributions and the achieved sum-rate capacity and capacity region are characterized. For comparison, the uplink and downlink sum-rates achieved by the linear zero-forcing scheme are also analyzed. To gain further insights, several case studies are presented by specializing the derived results into various array structures, including the planar CAPA, linear CAPA, and planar spatially discrete array (SPDA). Numerical results are provided to reveal that the channel capacity achieved by CAPAs converges towards a finite upper bound as the aperture size increases; and CAPAs offer superior capacity over the conventional SPDAs.

A novel time-efficient framework is proposed for improving the robustness of a broadband multiple-input multiple-output (MIMO) system against unknown interference under rapidly-varying channels. A mean-squared error (MSE) minimization problem is formulated by optimizing the beamformers employed. Since the unknown interference statistics are the premise for solving the formulated problem, an interference statistics tracking (IST) module is first designed. The IST module exploits both the time- and spatial-domain correlations of the interference-plus-noise (IPN) covariance for the future predictions with data training. Compared to the conventional signal-free space sampling approach, the IST module can realize zero-pilot and low-latency estimation. Subsequently, an interference-resistant hybrid beamforming (IR-HBF) module is presented, which incorporates both the prior knowledge of the theoretical optimization method as well as the data-fed training. Taking advantage of the interpretable network structure, the IR-HBF module enables the simplified mapping from the interference statistics to the beamforming weights. The simulations are executed in high-mobility scenarios, where the numerical results unveil that: 1) the proposed IST module attains promising prediction accuracy compared to the conventional counterparts under different snapshot sampling errors; and 2) the proposed IR-HBF module achieves lower MSE with significantly reduced computational complexity.

Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation task offloading in MEC systems, the large data volume and complex task computation of artificial intelligence involved intelligent computation task offloading have increased greatly. To address this challenge, we propose a MEC system for multiple base stations and multiple terminals, which exploits semantic transmission and early exit of inference. Based on this, we investigate a joint semantic transmission and resource allocation problem for maximizing system reward combined with analysis of semantic transmission and intelligent computation process. To solve the formulated problem, we decompose it into communication resource allocation subproblem, semantic transmission subproblem, and computation capacity allocation subproblem. Then, we use 3D matching and convex optimization method to solve subproblems based on the block coordinate descent (BCD) framework. The optimized feasible solutions are derived from an efficient BCD based joint semantic transmission and resource allocation algorithm in MEC systems. Our simulation demonstrates that: 1) The proposed algorithm significantly improves the delay performance for MEC systems compared with benchmarks; 2) The design of transmission mode and early exit of inference greatly increases system reward during offloading; and 3) Our proposed system achieves efficient utilization of resources from the perspective of system reward in the intelligent scenario.

A novel multiple-input multiple-output (MIMO) cognitive radio (CR) system is proposed in this work. Specifically, the underly secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The secondary base station (SBS) beamformers and the STAR-RIS coefficients are jointly designed to maximize the sum rate of secondary users (SUs) considering the power limitation at the SBS, interference limitation at the primary users (PUs), and the coefficients constraint for the STAR-RIS. The block coordinate descent method is invoked to tackle the formulated optimization problem. In each iteration, the beamformers at the SBS are optimized by solving a quadratically constrained quadratic program problem, and the passive STAR beamforming problem is solved with the successive convex approximation-based algorithm. Simulation results show that the proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system.

A near-field sensing (NISE)-enabled user tracking scheme is proposed. Compared to conventional angle-only sensing in the far-field scenario, NISE offers the capability of joint angle and distance sensing. In the proposed user tracking scheme, a deep unfolding neural network (DUNN)-based method is employed to sense radial and transverse velocities, which can further facilitate predicting the user’s location in the next time instant. Specifically, the DUNN is training on a synthesized dataset to update trainable parameters in an offline manner. Then, the DUNN is implemented online to extract the radial and transverse velocities from the received echo signal. Moreover, an online fine-tuning module is attached to the DUNN to refine the output of the pre-trained DUNN. Finally, based on estimated velocities, the user position in the consecutive time instant can be predicted, thus enabling user tracking. Simulation results show that the proposed scheme can extract velocities from echo signals and track the user accurately.

A multiple unmanned aerial vehicles (UAVs) enabled integrated sensing and communication (ISAC) system is investigated. In contrast to existing UAV-enabled ISAC systems assuming static users or 2D UAV trajectory, we consider a practical roaming user scenario and a 3D deployment for UAVs. Then, a joint trajectory and beamforming optimization problem is formulated for maximizing the long-term sum data rate, subject to the transmitting power constraint and ensuring beam pattern gain constraint for sensing target. We proposed a two-step approach for against the dynamic scenario: 1) a K-means based hierarchical user association algorithm is proposed to renew the user association periodically. 2) a hybrid reward multi-agent proximal policy optimization (HR-MAPPO) algorithm is proposed, which decomposes the complex combined reward into a team reward and an individual reward. Numerical results demonstrate that the proposed HR-MAPPO algorithm can outperform conventional single-agent and multi-agent RL algorithms by maintaining high scores on both sum data rate and beam pattern gain.

An extremely large-scale antenna array (ELAA) based integrated sensing and communication (ISAC) system is investigated. In contrast to existing near-field ISAC systems, we consider the complexity of beamforming design and antenna selection for ELAA under a user-roaming environment. The formulated problem is maximizing long-term sum data rate by jointly optimizing antenna selection and beamforming, subject to the constraints of transmitting power and sensing targets’ beam pattern gain. We propose a dimension reduction assisted multi-agent proximal policy optimization (DR-MAPPO) algorithm against the dynamic scenario: 1) an OrderConv layer is designed, which reduces the input dimension by removing the channel state information of unselected antenna while keeping the order information of selected ones, 2) multiple agents are employed to execute antenna selection and beamforming in a distributed manner. Numerical results demonstrate that the proposed DR-MAPPO algorithm improve the computational efficiency of joint antenna selection and beamforming optimization by reducing the size of the input dimension while obtaining comparable data rates and beampattern gain for sensing targets.

The cell-free massive multiple-input multiple-output (CF mMIMO) network is emerging as an innovative technology that utilizes numerous access points (APs) distributed across a region to provide services for users through coherent transmission and reception. To enhance the coverage of the CF mMIMO network, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the CF mMIMO network. In the STAR-RIS aided CF mMIMO network, beamforming for the downlink of mobile users becomes exceptionally complex and challenging. Therefore, we propose a beamforming algorithm based on soft actor-critic (SAC), which can jointly optimize the beamforming of APs and phase shifts and amplitude coefficients of STAR-RISs. Simulation results demonstrate that the proposed SAC-based beamforming algorithm significantly maximizes the sum-rate of the STAR-RIS aided CF mMIMO network, outperforming the no STAR-RIS aided CF mMIMO network in terms of meeting quality of service (QoS) requirement.

The beamforming performance of the uniform circular array (UCA) in near-field wideband communication systems is investigated. Firstly, the unique beam squint effect in near-field wideband UCA systems is analyzed in both the distance and angular domains. It is demonstrated that the generated beams at different frequencies are focused at different locations, resulting in significant beamforming loss. To alleviate this unique beam squint effect and facilitate beamfocusing, an energy-efficient beamforming scheme based on the true-time delay (TTD) architecture is proposed. Specifically, the phase shifters (PSs) and the time delay of TTDs are designed based on the analytical formula for beamforming gain. Additionally, the minimum number of TTDs required to achieve a predetermined beamforming gain while minimizing energy consumption is quantified. Numerical results show that the proposed beamforming scheme effectively eliminates the near-field beam squint and outperforms the conventional schemes in terms of spectral efficiency and energy efficiency.

The performance of continuous aperture array (CAPA)-based wireless communications is analyzed in an uplink scenario. An analytical framework is proposed to characterize uplink CAPA-based transmission using electromagnetic field theories. On this basis, new expressions are derived for the channel capacity in a single-user scenario and the sum-rate capacity in a multiuser scenario, along with the capacity-achieving decoding schemes. These findings are proved to differ greatly from those established for conventional spatially discrete (SPD) arrays. Numerical results are provided to demonstrate that CAPA offers significant capacity gains compared to the SPD array.

The concept of aperture selection is proposed for continuous aperture array (CAPA)-based communications. The achieved performance is analyzed in an uplink scenario by considering both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. In the LoS scenario, the optimal selection strategy is demonstrated to follow the nearest neighbor criterion, and the resulting signal-to-noise ratio (SNR) is analyzed. In the NLoS scenario, the achieved outage probability along with the diversity order is revealed. Numerical results are provided to demonstrate that aperture selection effectively maintains satisfactory performance by leveraging selection diversity while simultaneously reducing the implementation complexity of CAPAs.

A personalized mobile edge generation (P-MEG) stable diffusion structure is proposed. The stable diffusion model is deployed on the edge server, enabling users to train personalized weights for customized generation tasks. To mitigate oscillations and accelerate convergence speed during user-personalized training, an effective constant scaling connection (CSC) with random model segmentation method is introduced. In addition, the stability of forward propagation in conditioned stable diffusion generation is explored. Furthermore, we theoretically analyze the benefits of CSC in enhancing the stability of user-personalized model training. The numerical results demonstrate that the proposed CSC method effectively assists the user in training personalized weights. Additionally, the CSC significantly stabilizes hidden feature oscillations and accelerates convergence speed during the training of personalized stable diffusion model.

The performance bounds of near-field sensing are studied for circular arrays, focusing on the impact of bandwidth and array size. The closed-form Cramér-Rao bounds (CRBs) for angle and distance estimation are derived, revealing the scaling laws of the CRBs with bandwidth and array size. Contrary to expectations, enlarging array size does not always enhance sensing performance. Furthermore, the asymptotic CRBs are analyzed under different conditions, unveiling that the derived expressions include the existing results as special cases. Finally, the derived expressions are validated through numerical results.

A novel reconfigurable intelligent surfaces (RIS) assisted near-field (NF) ISAC system is investigated, where the spherical wave propagation environment is utilized to elevate the radio sensing performance. Except for the conventional RIS, the sensor elements are embedded on the RIS surface to conduct the radios sensing functionality. By exploiting the symmetry property of the steering vector, a new expression of position error bound (PEB) is derived to unveil the impact of the sensor deployment. To balance the radio sensing performance and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the number of sensor elements and the passive beamforming (BF). To solve this non-convex problem, a joint geometric programming element-wise (JGPE) algorithm is proposed. The successive convex approximation for geometric programming is invoked to optimize the number of sensor elements while the element-wise algorithm is adopted to optimize the passive BF. Numerical results demonstrated that the proposed algorithm reach the least PEB and cost function value among the benchmarks.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled non-orthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, a beam-steering scheme is proposed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. Numerical results validate that the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme.

The application of inverse synthetic aperture radar (ISAR) is investigated in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. In contrast to velocity sensing of a point target of most ISAC works, ISAR enables rotational velocity sensing to obtain the cross-range values of different scatterers on an extended target. To utilize this characteristic, we initially derive the ISAR OFDM received signal reconstruction in the frequency domain, which demonstrates that the ISAR OFDM echo signal can be equivalent to the signal received by an array, including the decoupled radial range and cross-range parameters. According to the derived signal model, a supporting parameter estimation algorithm based on the equivalent array form is proposed to estimate the range and cross-range parameters for resolvable scatterers on the extended target. Finally, numerical results confirm the effectiveness of utilizing ISAR sensing in wideband ISAC systems.

Capacity limits of near-field (NF) multiple access channel (MAC) is characterized. Closed-form expressions are derived for the sum-rate capacity and the capacity region under a two-user scenario. These results are further extended to scenarios with an arbitrary number of users. Further insights are gleaned by exploring special cases, including scenarios with infinitely large array apertures and co-directional users. Theoretical and numerical results are presented and compared with far-field communications to demonstrate that: i) the NF capacity of the MAC converges to finite values rather than growing unboundedly as the number of array elements increases; ii) the MAC capacity with co-directional users can be improved by using the additional range dimensions in NF channels to reduce inter-user interference.

Mobile edge generation (MEG) is an emerging technology that allows the network to meet the challenging traffic load expectations posed by the rise of generative artificial intelligence (GAI). A novel MEG model is proposed for deploying GAI models on edge servers (ES) and user equipment (UE) to jointly complete test-to-image generation tasks. In the generaation task, the user uploads the text prompt and the ES and UE will cooperatively generate the image for the user. To enable the data transmission exchange between the ES and the UE, a seed based MEG protocol is employed, where a coded latent feature is created as a generation seed. A pre-trained latent diffusion model (LDM) is invoked to generate the latent feature, and a compression coding technique is proposed for compressing the latent features. The proposed MEG enabled text-to-image generation system is evaluated in terms of image quality and transmission overhead. The numerical results indicate that, compared to the conventional centralized generation-and-downloading scheme, the symbol number of the transmission of MEG is materially reduced. In addition, the proposed compression coding approach can improve the quality of generated images under low signal-to-noise ratio (SNR) conditions.

WiFi human sensing is highly regarded for its low-cost and privacy advantages in recognizing human activities. However, its effectiveness is largely confined to controlled, single-user, line-of-sight settings, limited by data collection complexities and the scarcity of labeled datasets. Traditional cross-modal methods, aimed at mitigating these limitations by enabling self-supervised learning without labeled data, struggle to extract meaningful features from amplitude-phase combinations. In response, we introduce AutoSen, an innovative automatic WiFi sensing solution that departs from conventional approaches. AutoSen establishes a direct link between amplitude and phase through automated cross-modal autoencoder learning. This autoencoder efficiently extracts valuable features from unlabeled CSI data, encompassing amplitude and phase information while eliminating their respective unique noises. These features are then leveraged for specific tasks using few-shot learning techniques. AutoSen’s performance is rigorously evaluated on a publicly accessible benchmark dataset, demonstrating its exceptional capabilities in automatic WiFi sensing through the extraction of comprehensive cross-modal features.

A downlink near-field integrated sensing and communication (ISAC) framework is proposed. A novel double-array structure at the BS is proposed, where an assisting receiver (AR) is attached to the main transmitter (MT) to enable the near-field communication (NFC) system with the ability of target positioning. The joint angle and distance Cramér-Rao bound (CRB) is derived and then minimized subject to the communication quality of ser-vice (QoS) requirement and the hybrid-analog-and-digital (HAD) structure constraint. A double-loop iterative algorithm utilizing the penalty dual decomposition (PDD) framework is proposed to tackle the non-convex problem. The numerical results show that: 1) The proposed ISAC system can locate the target in both angle and distance domains; 2) The performance of the HAD ISAC approaches the performance of fully digital (FD) ISAC when the communication QoS requirement is not stringent.

An active-sensing-based learning algorithm is proposed to solve the near-field beam alignment problem with the aid of wavenumber-domain transform matrices (WTMs). Specifically, WTMs can transform the antenna-domain channel into a sparse representation in the wavenumber domain. The dimensions of WTMs can be further reduced by exploiting the dominance of line-of-sight (LoS) links. By employing these lower-dimensional WTMs as mapping functions, the active-sensing-based algorithm is executed in the wavenumber domain, resulting in an acceleration of convergence. Compared with the codebook-based beam alignment methods, the proposed method finds the optimal beam pair in a ping-pong fashion, thus avoiding high training overheads caused by beam sweeping. Finally, the numerical results validate the effectiveness of the proposed method.

A novel semantic communication (SC)-assisted secrecy transmission framework is proposed. In particular, the legitimate transmitter (Tx) sends the superimposed semantic and bit stream to the legitimate receiver (Rx), where the information may be eavesdropped by the malicious node (EVE). As the EVE merely has the conventional bit-oriented communication structure, the semantic signal acts as the type of beneficial information-bearing artificial noise (AN), which not only keeps strictly confidential to the EVE but also interferes with the EVE. The ergodic (equivalent) secrecy rate over fading wiretap channels is maximized by jointly optimizing the transmit power, semantic-bit power splitting ratio, and the successive interference cancellation decoding order at the Tx, subject to both the instantaneous peak and long-term average power constraints. To address this non-convex problem, both the optimal and suboptimal algorithms are developed by employing the Lagrangian dual method and the successive convex approximation method, respectively. Numerical results show that the proposed SC-assisted secrecy transmission scheme can significantly enhance the physical layer security compared to the baselines using the conventional bit-oriented communication and no-information-bearing AN. It also shows that the proposed suboptimal algorithm can achieve a near-optimal performance.

This work studies a low-complexity design for re-configurable intelligent surface (RIS)-aided multiuser multiple-input multiple-output systems. The base station (BS) applies receive antenna selection to connect a subset of its antennas to the available radio frequency chains. For this setting, the BS switching network, uplink precoders, and RIS phase-shifts are jointly designed, such that the uplink sum-rate is maximized. The principle design problem reduces to an NP-hard mixed-integer optimization. We hence invoke the weighted minimum mean squared error technique and the penalty dual decomposition method to develop a tractable iterative algorithm that approxi-mates the optimal design effectively. Our numerical investigations verify the efficiency of the proposed algorithm and its superior performance as compared with the benchmark.

The advent of mobile edge computing (MEC) technology offers new prospects for executing demanding applications close to the user. However, complex applications like intelligent transportation and autonomous driving pose modeling and problem-solving challenges due to inter-user service logic correlations. Therefore, we construct a model that considers the terminal's mobility, time-varying channel status, and inter-user task dependencies and formulate a problem aiming to optimize the task completion delay and the energy consumption weighted cost in a dynamic MEC scenario. To resolve this problem, a Double Deep Q Network (DDQN)-based algorithm is developed for task offloading, while integrated sub channel allocation and transmit power control constitute part of the interaction with the dynamic environment to generate the reward signal, optimizing the long-term system performance. Comprehensive simulations verify that the proposed algorithm outperforms the comparative methods in terms of reducing the cost, and its adaptability in different scenarios has also been validated and analyzed.

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. A se-rial and a hybrid serial-parallel TTD configuration are inves-tigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits a cumulative time delay through multiple TTDs, which potentially alleviates the maximum delay requirements on the TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. In this context, a hybrid TTD configuration is proposed as a compromise solution. More-over, the wideband near-field beamforming design for different configurations is studied for maximizing the spectral efficiency in single-user systems. In particular, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. Our numerical results confirm the effectiveness of the proposed designs.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station to send the information beams for information transmission while charging energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer and the baseband digital beamformers. To tackle the non-convex optimization problem, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, a low-complexity two-stage algorithm to reduce the high computational complexity caused by the large number of antennas is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance; and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

Multiple access is one of the primary issues for multi-user semantic communication systems. In this paper, we propose a novel pair of semantic difference (SeD) aware NOMA transceivers for downlink semantic-based image transmission, which mitigates the semantic-level interference among semantic streams. In specific, a SeD-aware superposition coding (SC) technique is proposed to suppress the semantic-level interference by coupling the semantic symbols of higher inter-feature semantic difference, which makes the interfering semantic symbols be identified and filtered out by the corresponding decoding function. The SeD-aware successive interference cancellation (SIC) technique further reduces the semantic-level interference by estimating the transmitted semantic symbols with the joint semantic and channel (JSC) autoencoder. Simulation results show that the proposed transceivers achieve comparable performance with benchmarks of OMA-aided transmission, while outperforming the benchmark of SeD-unaware NOMA transceivers in terms of the quality of reconstructed images and outperforming both benchmarks in terms of semantic transmission efficiency.

A near-field multi-input multi-output (MIMO) multi-user downlink system is investigated. To achieve efficient beam tracking in near field, the trajectories of mobile users (MUs) are first predicted, and then successive hybrid beamfocusing with data stream allocation is performed to maximize the throughput. Specifically, a digit-aware location prediction framework based on Transformer is proposed to predict the subsequent locations of MUs. Through attending to the individual decimal digits of the MUs' locations, the prediction error can be reduced from the digit perspective. We then propose a dual-tiered proximal policy optimization algorithm to learn the adaptive hybrid beam-focusing and data stream allocation according to the predicted MUs' movement. The policy of agent is hierarchically designed for effective dimensionality reduction of the large-scale action space. The numerical results demonstrate that 1) Our proposed algorithms outperform the baselines in terms of throughput with high predictive accuracy and beamfocusing gain; 2) The proposed beam tracking scheme can achieve a similar throughput to the perfect CSI scheme, while the performance gap of the non-tracking scheme is 53.2%; 3) Compared to the fixed data stream allocation, the proposed adaptive data stream allocation benefits a performance gain which escalates with an increasing number of total data streams.

In this paper, a near-field ISAC framework is proposed with an accurate channel model, in which the loss caused by effective aperture and polarization mismatch are considered. Based on the proposed model, sensing and communication (S&C) performance are analyzed in terms of three different designs: the communications-centric design, the sensing-centric design, and the Pareto optimal design. Within each design, sensing rates (SRs) and communication rates (CRs) are derived. Moreover, the attainable SR-CR regions of the near-field ISAC are characterized. Numerical results reveal that 1) the adopted channel model is more accurate than the conventional models within near field; 2) ISAC achieves a more extensive rate region than the conventional frequency-division S&C.

This paper proposes a cluster-based Non-orthogonal multiple access framework for integrated sensing and communications (ISAC). Based on the proposed model, we investigate three typical ISAC precoding designs: sensing-centric design, communications-centric design, and Pareto optimal design. Under each scenario, the sensing rate (SR), communications rate (CR), and their asymptotic expressions in high signal-to-noise ratio (SNR) regime are derived. High-SNR slopes are also obtained to gain better insights. Finally, the SR-CR rate regions achieved by ISAC and the conventional frequency-division sensing and communications (FDSAC) are studied. Numerical results reveal that ISAC outperforms FDSAC in terms of both SR and CR, and is able to achieve a more extensive rate region.

Existing few-shot semantic segmentation methods typically rely on a one-way flow of category information from support to query, ignoring the impact of intra-class diversity. To address this, drawing inspiration from cybernetics, we introduce a Query Feedback Branch (QFB) to propagate query information back to support, generating a query-related support prototype that is more aligned with the query. Subsequently, a Query Amplifier Branch (QAB) is employed to amplify target objects in the query using the acquired support prototype. To further improve the model, we propose a Query Rectification Module (QRM), which utilizes the prediction disparity in the query before and after support activation to identify challenging positive and negative samples from ambiguous regions for query self-rectification. Furthermore, we integrate the QFB, QAB, and QRM into a feedback and rectification layer and incorporate it into an iterative pipeline. This configuration enables the progressive enhancement of bidirectional reciprocative flow of category information between query and support, effectively providing query-adaptive support information and addressing the intra-class diversity problem. Extensive experiments conducted on both PASCAL-5i and COCO-20i datasets validate the effectiveness of our approach. The code is available at https://github.com/LIUYUANWEI98/IFRNet .

Physical adversarial attacks can deceive deep neural networks (DNNs), leading to erroneous predictions in real-world scenarios. To uncover potential security risks, attacking the safety-critical task of person detection has garnered significant attention. However, we observe that existing attack methods overlook the pivotal role of the camera, involving capturing real-world scenes and converting them into digital images, in the physical adversarial attack workflow. This oversight leads to instability and challenges in reproducing these attacks. In this work, we revisit patch-based attacks against person detectors and introduce a camera-agnostic physical adversarial attack to mitigate this limitation. Specifically, we construct a differentiable camera Image Signal Processing (ISP) proxy network to compensate for the physical-to-digital transition gap. Furthermore, the camera ISP proxy network serves as a defense module, forming an adversarial optimization framework with the attack module. The attack module optimizes adversarial patches to maximize effectiveness, while the defense module optimizes the conditional parameters of the camera ISP proxy network to minimize attack effectiveness. These modules engage in an adversarial game, enhancing cross-camera stability. Experimental results demonstrate that our proposed Camera-Agnostic Patch (CAP) attack effectively conceals persons from detectors across various imaging hardware, including two distinct cameras and four smartphones.

In this paper, the design of non-uniform antenna topology for 3D massive MIMO arrays in near-field communications is investigated. Specifically, the near-field spherical wavefront radiation characteristics are considered to accurately model the variations of signal phase across array elements. Subsequently, the closed-form expressions of the per-user signal-to-interference noise ratio (SINR) and achievable sum rate for a multi-user MIMO system with maximum-ratio transmission (MRT) precoding are derived. The focus is on the maximization of the achievable sum rate by optimizing the non-uniform 3D antenna array topology. Since the optimization problem exhibits highly nonlinear and nonconvex characteristics, an enhanced particle swarm optimization (EPSO) algorithm is proposed to effectively solve it. Numerical results demonstrate the superiority of the proposed non-uniform 3D array topology in near-field communications for enhancing the achievable sum rate.

Due to the high-mobility of aeronautical communications, real-time hybrid beamforming (HBF) is indispensable. In this paper, a novel HBF scheme in the aeronautical multiple-input multiple-output orthogonal frequency division multiplexing systems is proposed with model-driven deep learning. In particular, we formulate a mean square error minimization problem with manifold constraints. As the conventional iterative optimization algorithm has high computational complexity, we propose a deep-unfolding HBF network which unfolds the iterations and introduces a set of trainable parameters. The proposed algorithm produces hybrid beamformer with several layers, which avoids cumbersome iteration procedures. Moreover, the deep-unfolding algorithm preserves the operation of the iterative optimizer, which brings in high interpretability. Numerical results show that the proposed HBF algorithm is superior to the conventional model-based counterparts in terms of reliability.

This paper proposes a framework of multiple-input multiple-output based non-orthogonal multiple access for Integrated sensing and communications (ISAC), which incorporates signal alignment to enhance system performance. Sensing rate (SR) and communication rate (CR) are derived under three different precoding designs: sensing-centric design, communications-centric design, and Pareto optimal design. Moreover, the SR-CR regions achieved by ISAC and frequency-division sensing and communications (FDSAC) are studied. Numerical results reveal that ISAC outperforms FDSAC in terms of both SR and CR and can achieve a broader rate region, clearly showcasing its superiority over the conventional FDSAC.

A novel non-orthogonal semantic communication (NSC) framework is proposed for facilitating high-efficiency multi-user semantic communications. The NSC technique enables non-orthogonal semantic streams among users by sharing the same resource block. A semantic superposition coding (SSC) and a semantic interference tolerated (SIT) decoding paradigm are proposed for the NSC transmitters and receivers, respectively. SSC encoders are a type of joint source-channel encoder enabled by deep learning (DL), which aims to superpose the semantic information for different users to a piece of semantic feature sequence. An SSC encoder at the access point (AP) is paired with several SIT decoders at different user equipment. By jointly training the SSC encoder and all SIT decoders, the SIT decoders can identify the desired semantic information for each user, and the semantic interferences introduced by SSC are mitigated. Simulation results reveal that the proposed NSC scheme considerably improves transmission efficiency. Meanwhile, at high compression ratios, the NSC scheme outperforms conventional orthogonal semantic communications in terms of accuracy gains.

A multiple unmanned aerial vehicle (UAV) enabled integrated sensing and communication (ISAC) system is investigated. In contrast to existing UAV-enabled ISAC systems assuming static users or 2-D UAV trajectory, we consider a practical roaming user scenario and a 3-D deployment for UAVs. Then, a joint trajectory and beamforming optimization problem is formulated for maximizing the long-term sum data rate, subject to the transmitting power constraint and ensuring beam pattern gain constraint for sensing target. To address the challenge caused by the dynamic and high dimensionality features, multiagent reinforcement learning (MARL) is employed for this partial observation Markov decision process (POMDP) problem. We proposed a two-step approach for against the dynamic scenario: 1) a K-means-based hierarchical user association algorithm is proposed to renew the user association periodically and 2) a hybrid reward multiagent proximal policy optimization (HR-MAPPO) algorithm is proposed, which decomposes the complex combined reward into a team reward and an individual reward. HR-MAPPO introduces a hyperparameter to control the proportion of team/individual action. Numerical results demonstrate that the proposed HR-MAPPO algorithm can outperform the conventional single-agent and multiagent RL algorithms by maintaining high scores on both the sum data rate and beam pattern gain.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the subproblem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the subproblem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

A millimeter wave (mmWave) line-of-sight (LOS) multi-input-multioutput (MIMO) system is studied, where spatial multiplexing is achieved through LOS transmissions in near-field areas. A comprehensive analysis of the achievable rate in mmWave LOS MIMO system with respect to the rotation of uniform linear arrays (ULAs) is provided for both full-rank orthogonal and rank-deficient LOS MIMO channels. 1) For full-rank orthogonal scenarios, the necessary and sufficient condition for the LOS MIMO system to have the maximum achievable rate is established. 2) For rank-deficient scenarios, a closed-form expression for the Degrees of Freedom (DoF) of mmWave LOS MIMO channels is derived. Subsequently, the alternating phase approximation-based (APA-based) hybrid precoding algorithm is proposed, where the number of radio frequency (RF) chains is determined by the DoF of mmWave LOS MIMO channels and the initial phase of iterative optimizations is determined according to the optimal digital precoder. Our numerical results confirm the effectiveness of our analysis and proposed algorithm. It is also unveiled that 1) the DoF can be maximized through a specific angle design and 2) compared to existing hybrid precoding algorithms, our proposed APA-based hybrid precoding algorithm exhibits superior robustness against the angular rotation of ULAs.

This paper proposes a joint sensing, communication and computation (JSCC) framework in unmanned aerial vehicle (UAV)-assisted systems, where multi-functional terminal devices (TDs) can perform high-accuracy radar sensing as well as offload computation data to an airborne mobile edge computing (MEC) server over the same frequency band. The key objective of the JSCC framework is to simultaneously minimize the transmitted sensing beampattern matching error whilst maximizing the minimum computation efficiency of TDs. This problem is formulated as a multi-objective optimization problem (MOOP) that jointly optimizes the transmit beampattern, computation offloading, and UAV trajectory. To achieve the computation-sensing trade-off region, we first transform the MOOP into a single-objective optimization problem (SOOP) via the 1-constraint method. To make it more tractable, a generalized Dinkelbach’s and successive convex approximation (GD-SCA) algorithm is proposed. Specifically, GD-SCA transfers the non-convex max-min fractional programming in the resultant SOOP by introducing a general auxiliary polynomial via generalized Dinkelbach’s algorithm. Thereafter, the transmit beampattern, computation offloading, and UAV trajectory optimization are decoupled into two nested subproblems, which can be iteratively solved by invoking successive convex approximation (SCA) method to handle the remaining non-convex components. The proposed GD-SCA can obtain high-quality suboptimal solutions of the original MOOP. We validate the effectiveness of the proposed algorithm by considering two multiple access techniques, i.e., non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA). Simulation results demonstrate that the proposed algorithm can achieve an improved computation-sensing trade-off region compared to conventional schemes especially when exploiting NOMA. Moreover, the multi-functional performance can be significantly improved while stringently guaranteeing both radar sensing and computation offloading requirements.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled nonorthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, two novel beamforming schemes are proposed. 1) For the case of the grouped users distributed in the same direction, a beam-steering scheme is developed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. 2) For the case of users randomly distributed, a beam-splitting scheme is proposed, where two subbeamformers are extracted from the single beamformer to serve different users in the same group. An alternating optimization (AO) algorithm is proposed for hybrid beamformer optimization, and the optimal power allocation is also derived. Numerical results validate that: 1) the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme and 2) the communication rate of the proposed transmission framework is sensitive to the imperfect distance knowledge of NUs but not to that of FUs.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging the energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer, the baseband digital information/energy beamformers, and the number of dedicated energy beams. To tackle the uncertain number of dedicated energy beams, a semidefinite relaxation-based rank-one solution construction method is proposed to obtain the optimal baseband digital beamformers under the fixed analog precoder. Based on the structure of the optimal baseband digital beamformers, it is proved that no dedicated energy beam is required in the near-field SWIPT. To further exploit this insight, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, to reduce the high computational complexity caused by the large number of antennas, a low-complexity two-stage algorithm is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

A near-field integrated sensing, positioning, and communication (ISPAC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users and carries out target sensing and positioning. A novel double-array structure is proposed to enable the near-field ISPAC at the BS. Specifically, a small-scale assisting transceiver (AT) is attached to the large-scale main transceiver (MT) to empower the communication system with the ability of sensing and positioning. Based on the proposed framework, the joint angle and distance Cramér-Rao bound (CRB) is first derived. Then, the CRB is minimized subject to the minimum communication rate requirement in both downlink and uplink ISPAC scenarios: 1) For downlink ISPAC, a downlink target positioning algorithm is proposed and a penalty dual decomposition (PDD)-based double-loop algorithm is developed to tackle the non-convex optimization problem. 2) For uplink ISPAC, an uplink target positioning algorithm is proposed and an efficient alternating optimization algorithm is conceived to solve the non-convex CRB minimization problem with coupled user communication and target probing design. Both proposed optimization algorithms can converge to a stationary point of the CRB minimization problem. Numerical results show that: 1) The proposed ISPAC system can locate the target in both angle and distance domains merely relying on single BS and limited bandwidths; and 2) the positioning performance achieved by the hybrid-analog-and-digital ISPAC approaches that achieved by fully digital ISPAC when the communication rate requirement is not stringent.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser near-field wideband communication system is investigated, in which a robust deep reinforcement learning (DRL) based algorithm is proposed to enhance the users’ achievable rate by jointly optimizing the active beamforming at the base station (BS) and passive beamforming at the STAR-RIS. To mitigate the beam split issue, the delay-phase hybrid precoding structure is introduced to facilitate wideband beamforming. Considering the coupled nature of the STAR-RIS phase-shift model, the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control, and the proposed algorithm controls the high-dimensional continuous action through hybrid action mapping. Additionally, to address the issue of biased estimation encountered by existing DRL algorithms, a softmax operator is introduced into the algorithm to mitigate this bias. Simulation results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

There is an urgent emergency for next-generation multiple access (NGMA) schemes to address the growing demands for our society by providing new services and practical scenarios to enhance our daily lives. The development of NGMA schemes is accelerating in terms of formulating capability objectives for the next-generation era and investigating state-of-art, promising, and tractable technology that may become part of the next-generation wireless communication systems in the future. Thus, it is anticipated that NGMA will address the requirements for the foundations of wireless communications (such as massive connectivity, stability, and wide-range coverage) and other applications (including data and computing assisted by machine learning, protocol designs, and other applications to encourage innovation and serve as the information backbone of society)..

The evolution of wireless communications has been significantly influenced by remarkable advancements in multiple access (MA) technologies over the past five decades, shaping the landscape of modern connectivity. Within this context, a comprehensive tutorial review is presented, focusing on representative MA techniques developed over the past 50 years. The following areas are explored: 1) the foundational principles and information-theoretic capacity limits of power-domain nonorthogonal multiple access (NOMA) are characterized, along with its extension to multiple-input multiple-output (MIMO)-NOMA; 2) several MA transmission schemes exploiting the spatial domain are investigated, encompassing both conventional space-division multiple access (SDMA)/MIMO-NOMA systems and near-field MA systems utilizing spherical-wave propagation models; 3) application of NOMA to integrated sensing and communications (ISAC) systems is studied. This includes an introduction to typical NOMA-based downlink (DL)/uplink (UL) ISAC frameworks, followed by an evaluation of their performance limits using a mutual information (MI)-based analytical framework; and 4) major issues and research opportunities associated with the integration of MA with other emerging technologies are identified to facilitate MA in the next-generation networks, i.e., next-generation multiple access (NGMA). Throughout this article, promising directions are highlighted to inspire future research endeavors in the realm of MA and NGMA.

Unmanned aerial vehicle (UAV) assisted communications have been regarded as an effective solution to provide instant services. This paper proposes a novel reconfigurable intelligent surface (RIS) assisted multi-UAV system, where ground users are formed as multicast groups and served by multiple UAVs with coordinated multi-point technique. The goal is to maximize the sum of the minimum rates for all groups by jointly optimizing the trajectories, the cooperative beamforming of the clustered UAVs, and the passive beamforming of the RIS. A hybrid learning scheme is proposed, integrating a multi-agent deep reinforcement learning algorithm, RES-QMIX, and a majorization-minimization (MM)-based alternating optimization. First, the RES-QMIX algorithm is proposed to optimize the trajectories of all UAVs. Then, the alternating optimization is invoked to decouple the joint beamforming into two sub-problems, and each is transformed into a convex quadratic cone programming problem with the MM algorithm. Moreover, the alternating optimization is employed to estimate the reward of the action in RES-QMIX algorithm, thus reducing the action space and achieving the joint optimization. Numerical results show that: 1) The proposed hybrid learning framework achieves fast convergence and outperforms heuristic algorithms; 2) The proposed system obtains a more significant communication rate than CoMP and RIS-only systems.

In this paper, we design a resource block (RB) oriented power pool (PP) for semi-grant-free non-orthogonal multiple access (SGF-NOMA) in the presence of residual errors resulting from imperfect successive interference cancellation (SIC). In the proposed method, the BS allocates one orthogonal RB to each grant-based (GB) user, and determines the acceptable received power from grant-free (GF) users and calculates a threshold against this RB for broadcasting. Each GF user as an agent, tries to find the optimal transmit power and RB without affecting the quality-of-service (QoS) and ongoing transmission of the GB user. To this end, we formulate the transmit power and RB allocation problem as a stochastic Markov game to design the desired PPs and maximize the long-term system throughput. The problem is then solved using multi-agent (MA) deep reinforcement learning algorithms, such as double deep Q networks (DDQN) and Dueling DDQN due to their enhanced capabilities in value estimation and policy learning, with the latter performing optimally in environments characterized by extensive states and action spaces. The agents (GF users) undertake actions, specifically adjusting power levels and selecting RBs, in pursuit of maximizing cumulative rewards (throughput). Simulation results indicate computational scalability and minimal signaling overhead of the proposed algorithm with notable gains in system throughput compared to existing SGF-NOMA systems. We examine the effect of SIC error levels on sum rate and user transmit power, revealing a decrease in sum rate and an increase in user transmit power as QoS requirements and error variance escalate. We demonstrate that PPs can benefit new (untrained) users joining the network and outperform conventional SGF-NOMA without PPs in spectral efficiency.

This paper proposes a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) emergency communication network. Multiple STAR-RISs are deployed to provide additional and intelligent transmission links between trapped users and UAV-mounted base station (BS). Each user selects the nearest STAR-RIS for uploading data, and NOMA is employed for users located at the same side of the same STAR-RIS. Considering practical requirements of post-disaster emergency communications, we formulate a throughput maximization problem subject to constraints on minimum average rate and maximum energy consumption, where the UAV trajectory, STAR-RIS passive beamforming, and time and power allocation are jointly optimized. Furthermore, we propose a Lagrange based reward constrained proximal policy optimization (LRCPPO) algorithm, which provides an adaptive method for solving the long-term optimization problem with cumulative constraints. Specifically, using Lagrange relaxation, the original problem is transformed into an unconstrained problem with a two-layer structure. The inner layer is solved by penalized reward based proximal policy optimization (PPO) algorithm. In the outer layer, Lagrange multipliers are updated by gradient descent. Numerical results show the proposed algorithm can effectively improve network performance while satisfying the constraints well. It also demonstrates the superiority of the proposed STAR-RIS assisted UAV NOMA network architecture over the benchmark schemes employing reflecting-only RISs and orthogonal multiple access.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access (NOMA) framework for finite blocklength (FBL) transmission is proposed. Considering the different communication requirements of Internet of Things devices (IoTDs), a novel design to achieve high-rate and low-error is proposed. Two operating protocols for STAR-RIS are considered, namely energy splitting (ES) and mode switching (MS). 1) For STAR-RIS with ES, an alternating optimization (AO) algorithm is proposed to handle the highly-coupled mixed integer programming problem. More particularly, a low-complexity received-signal-strength-based device pairing scheme is proposed. Based on the given device pair, the closed-form solutions for the power allocation problem are obtained. The transmitting and reflecting coefficient optimization problem is solved by exploiting the successive convex approximation and semidefinite relaxation methods. 2) For STAR-RIS with MS, a double-layer penalty-based (DLPB) algorithm is proposed to tackle the newly introduced binary amplitude constraints. Numerical results reveal that: i) the proposed AO and DLPB algorithms can converge within a few iteration times; ii) the FBL transmission performance can be improved by employing the proposed STAR-RIS framework compared with conventional transmitting/reflecting-only RISs; iii) NOMA is capable of enhancing FBL rate while guaranteeing the reliability constraints compared with orthogonal multiple access.

The joint uplink/downlink (JUD) design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is conceived in support of both uplink (UL) and downlink (DL) users. Furthermore, the dual STAR-RISs (D-STAR) concept is conceived as a promising architecture for 360-degree full-plane service coverage, including UL/DL users located between the base station (BS) and the D-STAR as well as beyond. The corresponding regions are termed as primary (P) and secondary (S) regions. Both BS/users exist in the P-region, but only users are located in the S-region. The primary STAR-RIS (STAR-P) plays an important role in terms of tackling the P-region inter-user interference, the self-interference (SI) from the BS and from the reflective as well as refractive UL users imposed on the DL receiver. By contrast, the secondary STAR-RIS (STAR-S) aims for mitigating the S-region interferences. The non-linear and non-convex rate-maximization problem formulated is solved by alternating optimization amongst the decomposed convex sub-problems of the BS beamformer, and the D-STAR amplitude as well as phase shift configurations. We also propose a D-STAR based active beamforming and passive STAR-RIS amplitude/phase (DBAP) optimization scheme to solve the respective sub-problems by Lagrange dual with Dinkelbach’s transformation, alternating direction method of multipliers (ADMM) with successive convex approximation (SCA), and penalty convex-concave procedure (PCCP). Our simulation results reveal that the proposed D-STAR architecture outperforms the conventional single RIS, single STAR-RIS, and half-duplex networks. The proposed DBAP of D-STAR outperforms the state-of-the-art solutions found in the open literature for different numbers of quantization levels, geographic deployment, transmit power and for diverse numbers of transmit antennas, patch partitions as well as D-STAR elements.

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. In this paper, aserialand ahybrid serial-parallelTTD configuration are investigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits acumulativetime delay caused by multiple TTDs, which potentially alleviates the maximum delay requirements on the individual TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. Therefore, a hybrid TTD configuration is proposed as a compromise solution. Furthermore, a power equalization approach is proposed to address the cumulative insertion loss of the serial and hybrid TTD configurations. Moreover, the wideband near-field beamforming design for different configurations is studied to maximize the spectral efficiency in both single-user and multiple-user systems. 1) For single-user systems, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. 2) For multi-user systems, a penalty-based iterative algorithm is developed to obtain a stationary point of the spectral efficiency maximization problem for the considered TTD configurations. In addition, a hybrid-forward-and-backward (HFB) implementation is proposed to enhance the performance of the serial configuration. Our numerical results confirm the effectiveness of the proposed designs and unveil that i) compared to the conventional parallel configuration, both the serial and hybrid configurations can significantly reduce the maximum time delays required for the individual TTDs and ii) the hybrid configuration excels in single-user systems, while the HFB serial configuration is preferred in multi-user systems.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology to reconfigure the radio propagation environment in the full space. Prior works on STAR-RISs have mostly considered the energy splitting operation protocol, which has high hardware complexity in practice. Moreover, the full and instantaneous channel state information (CSI) is always assumed available for designing the STAR-RIS nearly passive beamforming, which, however, is practically difficult to obtain due to the large number of STAR-RIS elements. To address these issues, we study the mode switching design in STAR-RIS aided non-orthogonal multiple access (NOMA) communication systems. Moreover, two efficient two-timescale (TTS) transmission protocols are proposed for different channel setups to maximize the respective average achievable sum-rate. Specifically, 1) for the case of line-of-sight (LoS) dominant channels, we propose the beamforming-then-estimate (BTE) protocol, where the long-term STAR-RIS transmission and reflection coefficients are optimized based on the statistical CSI only, while the short-term power allocation at the base station (BS) is designed based on the estimated effective fading channels of all the users; 2) for the case of rich scattering environments, we propose an alternative partition-then-estimate (PTE) protocol, where the BS first determines the long-term STAR-RIS surface-partition strategy based on the path-loss information only, with each subsurface being assigned to one user; and then the BS estimates the instantaneous subsurface channels associated with the users and designs its power allocation and STAR-RIS phase-shifts accordingly. For the two proposed transmission protocols, we further propose efficient algorithms to solve the respective long-term and short-term optimization problems. Moreover, we show that both proposed transmission protocols substantially reduce the channel estimation overhead as compared to the existing schemes based on full instantaneous CSI. Last, simulation results validate the superiority of our proposed transmission protocols as compared to various benchmarks. It is shown that the BTE protocol outperforms the PTE protocol when the number of STAR-RIS elements is large and/or the LoS channel components are dominant, and vice versa.

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.

Non-orthogonal multiple access (NOMA) is capable of serving different numbers of users in the same time-frequency resource element, and this feature can be leveraged to carry additional information. In the orthogonal frequency division multiplexing (OFDM) system, a novel enhanced NOMA scheme called NOMA with informative envelope (NOMA-IE) is proposed to explore extra flexibility from the envelope of NOMA signals. In this scheme, data bits are conveyed by the quantified signal envelope in addition to classic signal constellations. The subcarrier activation patterns of different users are jointly decided by the envelope former at the transmitter of NOMA-IE. At the receiver, successive interference cancellation (SIC) is employed, and the envelope detection coefficient is introduced to eliminate the error floor. Theoretical expressions of spectral efficiency, energy efficiency, and detection complexity are provided first. Then, considering the binary phase shift keying modulation, the block error rate and bit error rate are derived based on the two-subcarrier element. The analytical results reveal that the SIC error and the index error are the main factors degrading the error performance. The numerical results demonstrate the superiority of the NOMA-IE over the OFDM and OFDM-NOMA in terms of the error rate performance when all the schemes have the same spectral efficiency and energy efficiency.

In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Finally, a joint multiple time-scale optimization algorithm with a two-layer loop structure is proposed. Simulation results show that: 1) the proposed multi-UAV cooperative computation MEC system outperforms the conventional MEC system without collaboration among UAVs; and 2) the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different network sizes, task requirements, and available resources.

In-network aggregation is a fundamental operation for massive packets in the Internet of Things (IoT). By aggregating ultra-small packets, the energy consumption for data transmission is not related to the packet number, while the average delay performance depends on the delay of all packets even if they are aggregated. In this paper, we propose a low-delay ultra-small packet transmission scheme with in-network aggregation in energy-harvesting multi-hop networks, where each device periodically transmits packets in a collect-wait-forward relaying manner. Considering the resulting extra waiting time during relaying, we first drive the tractable form of the average end-to-end delay by problem transformation. By characterizing the two-dimensional evolution property from the perspective of both hops and time, the delay minimization problem is reformulated as an infinite-horizon average-cost Markov decision process with a two-dimensional optimality equation. To deal with the curse of dimensionality, we decompose the global Bellman equation into several per-device local relay selection problems. Based on the problem decomposition, we propose a distributed ultra-Small Packet Aggregation Relay SElection (SPARSE) algorithm via stochastic learning. The convergence is further proved theoretically and verified by simulation. Simulation results reveal that the proposed scheme achieves significant performance gain over the baselines for ultra-small packets.

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm that enables massive edge devices to train machine learning models collaboratively. Although various communication schemes have been proposed to expedite the FL process in resource-limited wireless networks, the unreliable nature of wireless channels was less explored. In this work, we propose a novel FL framework, namely FL with gradient recycling (FL-GR), which recycles the historical gradients of unscheduled and transmission-failure devices to improve the learning performance of FL. To reduce the hardware requirements for implementing FL-GR in the practical network, we develop a memory-friendly FL-GR that is equivalent to FL-GR but requires low memory of the edge server. We then theoretically analyze how the wireless network parameters affect the convergence bound of FL-GR, revealing that minimizing the average square of local gradients’ staleness (AS-GS) helps improve the learning performance. Based on this, we formulate a joint device scheduling, resource allocation and power control optimization problem to minimize the AS-GS for global loss minimization. To solve the problem, we first derive the optimal power control policy for devices and transform the AS-GS minimization problem into a bipartite graph matching problem. Through detailed analysis, we further transform the bipartite matching problem into an equivalent linear program which is convenient to solve. Extensive simulation results on three real-world datasets (i.e., MNIST, CIFAR-10, and CIFAR-100) verified the efficacy of the proposed methods. Compared to the FL algorithms without gradient recycling, FL-GR is able to achieve higher accuracy and fast convergence speed. In addition, the proposed device scheduling and resource allocation algorithm also outperforms the benchmarks in accuracy and convergence speed.

Traditional scheduling algorithms make decisions based solely on the channel conditions, without considering the transmitted semantic content. However, in multi-modal semantic communication systems, there is redundancy and variance in the importance of semantic features across modalities for task performance. To address this issue, we propose a novel feature scheduling and error probability control scheme that balances channel diversity and semantic diversity in semantic communication systems. Specifically, we first introduce a semantic feature importance metric to measure each feature’s contribution to the inference performance of semantic task. Using this metric, we formulate and solve an optimization problem to reduce overall latency while guaranteeing semantic task performance. Our detailed analysis examines feature selection strategies and transmission rate optimization to illustrate scheduling decisions based on both channel fading and semantic content. Consequently, we develop both optimal and low-complexity feature transmission scheduling schemes based on the optimization solution. Extensive experiments over multi-modal semantic communication systems validate that the semantic feature importance metric can reveal the importance of features from different modalities and accordingly affect their transmission rate. Additionally, the proposed schemes significantly reduce the system latency compared to the traditional methods.

The three-dimensional (3D) beamforming property of the uniform circular array (UCA) in near-field wideband communication systems is comprehensively analyzed in both the distance and angular domains. It is rigorously demonstrated that the beam focal point only exists at a specific frequency in wideband UCA systems, resulting in significant beamforming loss. To facilitate near-field beamfocusing and alleviate the beam squint effect, the true-time delay (TTD)-based beamforming architecture is exploited. In particular, two wideband beamforming optimization approaches leveraging TTD units are proposed. 1)Analytical approach: In this approach, the phase shifters (PSs) and the time delay of TTD units are designed based on the analytical formula for beamforming gain. Following this design, the minimum number of TTD units required to achieve a predetermined beamforming gain is quantified. 2)Joint-optimization approach: In this method, the PSs and the TTD units are jointly optimized under practical maximum delay constraints to approximate the optimal unconstrained analog beamformer. Specifically, an efficient alternating optimization algorithm is proposed, where the PSs and the TTD units are alternately updated using either the closed-form solution or the low-complexity linear search approach. Extensive numerical results demonstrate that 1) the proposed beamforming schemes effectively mitigate the beam squint effect, and 2) the joint-optimization approach outperforms the analytical approach in terms of array gain and achievable spectral efficiency.

A simultaneously transmitting and reflecting surface (STARS) assisted multi-user downlink multiple-input signal-output (MISO) multi-cellular edge caching system is investigated. The deployment of STARS enhances the coverage of base stations (BSs), particularly at cellular boundaries. However, this advancement introduces a complex user association issue that necessitates the consideration of both caching state and channel state information (CSI). In this paper, we formulate a joint optimization problem involving content caching, user association, active beamforming at BS, and passive beamforming at STARS for minimizing long-term power consumption. We propose two algorithms for the formulated problem: 1) A two time-scale cooperative twin delayed deep deterministic policy gradients (TD3). Considering the distinct time scales of the pushing and delivering phases in edge caching, the Markov decision process (MDP) models of dual time scales are constructed and two deep reinforcement learning (DRL) agents work together to jointly address the optimization problem. 2) A bio-inspired DRL framework, especially, a particle swarm optimization (PSO)-inspired TD3 algorithm is introduced in detail. Inspired by the behavior of the biological population in nature, this algorithm regards agents as individuals and enables the concurrent training of multiple agents while they interact with global information via a biological population information interaction mode, thereby enhancing the performance of power optimization. The numerical results demonstrate that the STARS-assisted multi-cellular edge caching system has advantages over traditional cellular systems, especially in scenarios where the number of mobile users and Zipf skewness factor is large. Moreover, the proposed two time-scale cooperative TD3 and PSO-inspired TD3 algorithms are superior in reducing network power consumption than conventional TD3.

A simultaneously transmitting and reflecting surface (STARS) enabled edge caching system is proposed for reducing backhaul traffic and ensuring the quality of service. A novel Caching-at-STARS structure, where a dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel conditions. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm. For the independent phase-shift Caching-at-STARS model, we develop a frequency-aware based twin delayed deep deterministic policy gradient (FA-TD3) algorithm that leverages user historical request information to serialize high-dimensional caching replacement decision variables. For the coupled phase-shift Caching-at-STARS model, we conceive a cooperative TD3 & deep-Q network (TD3-DQN) algorithm comprised of FA-TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) Caching-at-STARS outperforms the RIS-assisted edge caching systems; 3) The proposed FA-TD3 and cooperative TD3-DQN algorithms are superior in reducing network power consumption than conventional TD3.

An active-sensing-based sense-then-train (STT) scheme is proposed for beam training in near-field multiple-input multiple-output (MIMO) systems. Compared to conventional codebook-based schemes, the proposed STT scheme is capable of not only addressing the complex spherical-wave propagation but also effectively exploiting the additional degrees-of-freedoms (DoFs). The STT scheme is tailored for both single-beam and multi-beam cases. 1) For the single-beam case, the STT scheme first utilizes a sensing phase to estimate a low-dimensional representation of the near-field MIMO channel in the truncated wavenumber domain. Then, in the subsequent training phase, the neural network modules at transceivers are updated online to align beams, utilizing sequentially received ping-pong pilots. This approach can efficiently obtain the aligned beam pair without relying on predefined codebooks or training datasets. 2) For the multi-beam case, based on the single-beam STT, a Gram-Schmidt method is further utilized to guarantee the orthogonality between beams in the training phase. Numerical results unveil that 1) the proposed STT scheme can significantly enhance the beam training performance in the near field compared to the conventional far-field codebook-based schemes, and 2) the proposed STT scheme can perform fast and low-complexity beam training, while achieving a near-optimal performance without full channel state information in both cases.

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For theindependent phase-shift model, a successive convex approximation-based algorithm is proposed; 2) For thecoupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system; 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional to the product of the distances from the base station to the PRIS and from the PRIS to the users, i.e., the productive attenuation. To tackle this problem, active RIS (ARIS) has been proposed to reconfigure the wireless propagation condition and alleviate the productive attenuation. In this paper, we investigate the physical layer security of the ARIS assisted non-orthogonal multiple access (NOMA) networks with the attendance of external and internal eavesdroppers. To be specific, the closed-form expressions of secrecy outage probability (SOP) and secrecy system throughput are derived by invoking both imperfect successive interference cancellation (ipSIC) and perfect SIC. The secrecy diversity orders of legitimate users are obtained at high signal-to-noise ratios. Numerical results are presented to verify the accuracy of the theoretical expressions and indicate that: i) The SOP of ARIS assisted NOMA networks exceeds that of PRIS-NOMA, ARIS/PRIS-assisted orthogonal multiple access (OMA); ii) Due to the balance between the thermal noise and residual interference, introducing excess reconfigurable elements at ARIS is not helpful to reduce the SOP; and iii) The secrecy throughput performance of ARIS-NOMA networks outperforms that of PRIS-NOMA and ARIS/PRIS-OMA networks.

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.

This paper studies an extremely large-scale reconfigurable intelligent surface (XL-RIS) empowered covert communication system in the near-field region. Alice covertly transmits messages to Bob with the assistance of the XL-RIS, while evading detection by Willie. To enhance the covert communication performance, we maximize the achievable covert rate by jointly optimizing the hybrid analog and digital beamformers at Alice, as well as the reflection coefficient matrix at the XL-RIS. An alternating optimization algorithm is proposed to solve the joint beamforming design problem. For the hybrid beamformer design, a semi-closed-form solution for fully digital beamformer is first obtained by a weighted minimum mean-square error based algorithm, then the baseband digital and analog beamformers at Alice are designed by approximating the fully digital beamformer via manifold optimization. For the XL-RIS’s reflection coefficient matrix design, a low-complexity alternating direction method of multipliers based algorithm is proposed to address the challenge of large-scale variables and unit-modulus constraints. Numerical results unveil that i) the near-field communications can achieve a higher covert rate than the far-field covert communications in general, and still realize covert transmission even if Willie is located at the same direction as Bob and closer to the XL-RIS; ii) the proposed algorithm can enhance the covert rate significantly compared to the benchmark schemes; iii) the proposed algorithm leads to a beam diffraction pattern that can bypass Willie and achieve high-rate covert transmission to Bob.

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for hybrid-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.

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.

A bidirectional integrated sensing and communication (ISAC) system is proposed, in which a pair of transceivers carry out two-way communication and mutual sensing. Both full-duplex and half-duplex operations in narrowband and wideband systems are conceived for the bidirectional ISAC. 1) For the narrowband system, the conventional full-duplex and half-duplex operations are redesigned to take into account sensing echo signals. Then, the transmit beamforming design of both transceivers is proposed for addressing the sensing and communication (S&C) tradeoff. A one-layer iterative algorithm relying on successive convex approximation (SCA) is proposed to obtain Karush-Kuhn-Tucker (KKT) optimal solutions. 2) For the wideband system, the new full-duplex and half-duplex operations are proposed for the bidirectional ISAC. In particular, the frequency-selective fading channel is tackled by delay pre-compensation and path-based beamforming. By redesigning the proposed SCA-based algorithm, the KKT optimal solutions for path-based beamforming for characterizing the S&C tradeoff are obtained. Finally, the numerical results show that: i) For both bandwidth scenarios,full-duplex mode may not always be preferable to half-duplex modedue to the presence of the sensing interference; and ii) For both duplex operations, it is sufficient to reuse communication signals for sensing in the narrowband system, while an additional dedicated sensing signal is required in the wideband system.

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, a stochastic geometry based analytical framework is proposed for secure simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) transmissions, where legitimate users (LUs) and eavesdroppers are randomly distributed. Both the time-switching protocol (TS) and energy splitting (ES) protocol are considered for the STAR-RIS. To characterize system performance, the channel statistics are first provided, and the Gamma approximation is adopted for general cascaded κ-μ fading. Afterward, the closed-form expressions for both the secrecy outage probability (SOP) and average secrecy capacity (ASC) are derived. To obtain further insights, the asymptotic performance for the secrecy diversity order and the secrecy slope are deduced. The theoretical results show that 1) the secrecy diversity orders of the strong LU and the weak LU depend on the path loss exponent and the distribution of the received signal-to-noise ratio, respectively; 2) the secrecy slope of the ES protocol achieves the value of one, higher than the slope of the TS protocol which is the mode operation parameter of TS. The numerical results demonstrate that: 1) there is an optimal STAR-RIS mode operation parameter to maximize the secrecy performance; 2) the STAR-RIS-NOMA significantly outperforms the STAR-RIS-orthogonal multiple access.

The reconfigurable intelligent surface (RIS) is a promising technology to provide smart radio environment. In contrast to the well-studied patch-array-based RISs, this work focuses on the metasurface-based RISs and simultaneously transmitting and reflecting (STAR)-RISs where the elements have millimeter or even molecular sizes. For these meticulous metasurface structures, near-field effects are dominant and a continuous electric current distribution should be adopted for capturing their electromagnetic response instead of discrete phase-shift matrices. Exploiting the electric current distribution, a Green’s function method based channel model is proposed. Based on the proposed model, performance analysis is carried out for both transmitting/reflecting-only RISs and STAR-RISs. 1) For the transmitting/reflecting-only RIS-aided single-user scenario, closed-formed expressions for the near-field/far-field boundary and the end-to-end channel gain are derived. Then, degrees-of-freedom (DoFs) and the power scaling laws are obtained. It is proved that the near-field channel exhibits higher DoFs than the far-field channel. It is also confirmed that when communication distance increases beyond the field boundary, the near-field power scaling law degrades to the well-known far-field result. 2) For the STAR-RIS-aided multi-user scenario, three practical STAR-RIS configuration strategies are proposed, namely power splitting (PS), selective element grouping (SEG), and random element grouping (REG) strategies. The channel gains for users are derived within both the pure near-field regime and the hybrid near-field and far-field regime. Finally, numerical results confirm that: 1) metasurface-based RISs are able to to outperform patch-array-based RISs, 2) the received power scales quadratically with the number of elements within the far-field regime and scales linearly within the near-field regime, and 3) for STAR-RISs, SEG has the highest near-field channel gain among the three proposed strategies and PS yields the highest DoFs for the near-field channel.

A novel non-orthogonal multiple access (NOMA) assisted full space integrated sensing and communication (ISAC) framework is proposed to elevate the radio sensing performance. Exploiting the simultaneously transmitting and reflecting RIS (STAR-RIS) to extend the half-space into full-space ISAC coverage intensifies the competition for wireless resources. To alleviate this fierce competition as well as ensure ISAC performance, the cluster-based NOMA (CB-NOMA) technique is employed to save the joint communication and sensing (C&S) beams. Furthermore, the dedicated sensing beam accompanied by the joint C&S beams supports the radio sensing functionality. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design, subject to communication requirements. To solve this non-convex problem, a block coordinate descent (BCD) based integral matrix algorithm is proposed to reach a suboptimal solution. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed to optimize the coupled variables. For the passive BF block, the penalty-based method is invoked. To further reduce the complexity of the passive BF design, a BCD-based element-wise algorithm is proposed, where the joint phase shift and amplitude coefficients of each STAR-RIS element are optimized one by one. Simulation results verified that our proposed algorithms achieve higher beampattern gain towards the intended targets than the benchmark schemes accompanying less mismatch error.

We investigate joint power allocation and decoding order selection (PA-DOS) aimed at enhancing the outage performance of non-orthogonal multiple access (NOMA) systems. By considering the diverse target rates of users, new important properties of NOMA are revealed: When users’ target rates satisfy certain conditions, the channel state information (CSI) is not required by PA-DOS to minimize the system outage probability, and different users’ outage probabilities can be minimized simultaneously; When such conditions are not satisfied, the opposite situation occurs. Following these properties, two PA-DOS strategies are designed regarding distinct user priorities, which ensure the minimization of the system outage probability and the user outage probability of the high-priority user. Especially, these strategies do not require CSI or only require one-bit CSI feedback depending on users’ target rates. Analytical and numerical results are provided to demonstrate that the proposed strategies significantly outperform the existing strategies in terms of both system and user outage performance. Furthermore, the results show that although under some target rates the minimum user outage probabilities cannot be simultaneously achieved, they can be closely approached at the same time in the high-signal-to-noise-ratio regime by the proposed strategies.

This paper investigates a reconfigurable intelligent surface (RIS) assisted cooperative multicell integrated sensing and communication (ISAC) system with multiple users and targets. In particular, the RIS is leveraged to assist the joint transmission of the multiple base stations (BSs) to multiple users, while assisting cooperative sensing by multiple BSs to perform multiple targets sensing. We formulate a problem for the purpose of minimizing the transmit power via jointly designing the transmit beamforming of the BSs and phase shifts of the RIS, while guaranteeing the achievable communication rate requirements and the sensing mutual information requirements. To address this non-convex problem, a high-quality alternating optimization algorithm is developed to split the intractable problem into two sub-problems. Specifically, with the given phase shifts of the RIS, the transmit beamforming sub-problem is addressed by semidefinite relaxation-based algorithm. A successive convex approximation (SCA) method-based and penalty function-based convex-concave procedure algorithm is proposed to tackle the RIS phase-shift optimization sub-problem. To reduce the computational complexity, an efficient low-complexity alternating optimization algorithm is developed. For the transmit beamforming design, an SCA method-based second-order cone programming algorithm is proposed, while for the RIS phase-shift design, a circle manifold optimization-based algorithm is introduced by utilizing penalty function. Simulation results validate the advancement of deploying RIS in enhancing the performance of cooperative multicell ISAC systems in terms of transmit power. Furthermore, our results illustrate the significant superiority of the proposed algorithms over the benchmark schemes.

The novel active simultaneously transmitting and reflecting surface (ASTARS) has recently received a lot of attention due to its capability to conquer the multiplicative fading loss and achieve full-space smart radio environments. This paper introduces the ASTARS to assist non-orthogonal multiple access (NOMA) communications, where the paring users are uniformly distributed within the service area. We design the independent reflection/transmission phase-shift controllers of ASTARS to align the phases of cascaded channels at pairing users. We derive new approximate and asymptotic expressions of the outage probability and ergodic data rate for ASTARS-NOMA networks in the presence of perfect/imperfect successive interference cancellation (pSIC/ipSIC). The diversity orders and multiplexing gains for ASTARS-NOMA are derived to provide more insights. Furthermore, the system throughputs of ASTARS-NOMA are investigated in both delay-tolerant and delay-limited transmission modes. The numerical results are presented and show that: 1) ASTARS-NOMA with pSIC outperforms ASTARS assisted-orthogonal multiple access (ASTARS-OMA) in terms of outage probability and ergodic data rate; 2) The outage probability of ASTARS-NOMA with pSIC/ipSIC can be further reduced within a certain range by increasing the power amplification factors; and 3) The system throughputs of ASTARS-NOMA are superior to that of ASTARS-OMA in both delay-limited and delay-tolerant transmission modes.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are gaining great attention for their ability to achieve full-space coverage. In this paper, the queue-aware STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure system stability. To tackle the challenge of infinite time periods for stability, the long-term stability-oriented problem is reformulated as a per-slot queue-weighted sum rate (QWSR) maximization problem using Lyapunov drift theory. Particularly, the allocated rate weight for each user is determined by the corresponding data queue at the base station (BS). By jointly optimizing the NOMA decoding order, the active beamforming coefficients at the BS, and the passive transmission and reflection coefficients at the STAR-RIS, three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). An equivalent-combined channel gain based scheme is proposed to obtain the desired decoding order. For ES, the highly coupled and non-convex problem is solved iteratively and alternatively by invoking the blocked coordinate descent and the successive convex approximation methods. This approach is further expanded to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem for MS. For TS, the problem is decomposed into two subproblems, each of which is solved similarly as ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves superior performance to the conventional schemes; ii) the reformulated QWSR maximization problem is proven to ensure the system stability; and iii) TS performs best in both the QWSR and the average queue length.

A simultaneously transmitting and reflecting surface (STARS) enabled two-phase integrated sensing and communications (ISAC) framework is proposed, where a novel bi-directional sensing-STARS architecture is devised to facilitate the full-space communication and sensing in a time-switching manner. Based on the proposed framework, a joint optimization problem is formulated, where the Cram$\acute {\text {e}}\text{r}$-Rao bound (CRB) for estimating the 2-dimension direction-of-arrival of the sensing target is minimized. Two cases are considered for sensing performance enhancement. 1) For the two-user case with the fixed number of sensors, an alternating optimization algorithm is proposed. In particular, the maximum number of deployable sensors is obtained in the closed-form expressions, where the maximum number of sensors is revealed to be only relevant to the QoS requirements of communications. 2) For the multi-user case with the variable number of sensors, an extended CRB (ECRB) metric is proposed to characterize the impact of the number of sensors on the sensing performance. A generic decoupling approach is proposed to convexify the non-convex ECRB expression. Based on this, a novel penalty-based double-loop (PDL) algorithm is proposed. Simulation results reveal that 1) the proposed PDL algorithm achieves a near-optimal performance with consideration of sensor deployment; 2) it is preferable to deploy more passive elements than sensors in terms of achieving optimal sensing performance.

A novel dynamic hybrid beamforming architecture is proposed to achieve the spatial multiplexing-power consumption tradeoff for near-field multiple-input multiple-output (MIMO) networks, where a switch module is integrated between the baseband digital and analog phase-shift module to control the number of activated RF chains. Based on this architecture, an optimization problem is formulated that maximizes the sum of achievable rates while minimizing the hardware power consumption. Both continuous and discrete phase shifters are considered. 1) For continuous phase shifters, a wavenumber-domain weighted minimum mean-square error (WD-WMMSE) algorithm is proposed, which exploits the sparsity of WD near-field channels to achieve the low-dimensional beamformer design. 2) For discrete phase shifters, a penalty-based layered iterative (PLI) algorithm is proposed. The closed-form analog and baseband digital beamformers are derived in each iteration. Simulation results demonstrate that: 1) the proposed dynamic beamforming architecture outperforms the conventional fixed hybrid beamforming architecture in terms of spatial multiplexing-power consumption tradeoff, and 2) the proposed algorithms achieve better performance than the other baseline schemes.

This paper investigates reconfigurable intelligent surface (RIS) aided multi-cell non-orthogonal multiple access (NOMA) networks with stochastic geometry methods. Under Rayleigh and Nakagami-m fading channels, we provide two types of approximate channel models to depict RIS channels, i.e., the N-fold convolution model and the curve fitting model. The analysis reveals that the N-fold convolution model is accurate and tractable when ignoring inter-cell interference, while the curve fitting model can evaluate the impact of inter-cell interference with a small error. The N-fold convolution model provides accurate diversity orders compared to other existing approaches such as the central limit model. Based on these channel models, we derive the closed-form analytical and asymptotic expressions of coverage probabilities and ergodic rates for two paired NOMA users. The analytical results demonstrate that: i) When we ignore inter-cell interference, the diversity order of the typical user is equal to the number of Rayleigh fading channels; and ii) For Nakagami-m fading channels with coefficientm, the diversity order is equal tomtimes of the channel number. Numerical results show that: i) RISs are capable of enhancing the coverage performance and ergodic rates of the proposed network; and ii) RISs provide extra flexibility for NOMA decoding orders.

In this paper, we propose a novel interference-suppressed semantic-aware non-orthogonal multiple access (IS-SNOMA) framework for downlink image transmission in the semantic-aware communication networks. The proposed IS-SNOMA is able to mitigate the inter-user interference in the non-orthogonal transmission for multiple semantic-oriented users (SU) or the coexistence of SUs and bit-oriented users (BU). 1) For the homogeneous transmission of semantic streams, we propose a pair of novel semantic difference (SeD) aware IS-SNOMA transceivers to accommodate multiple SUs over the same resource block. A novel SeD-aware superposition coding (SeDSC) technique and SeD-aware successive interference cancellation (SeDSIC) technique are specially designed to mitigate the semantic-level interference. 2) For the heterogeneous transmission of semantic-bit streams, we develop a pair of syntactic difference (SyD) aware IS-SNOMA transceivers to multiplex channels for SUs and BUs. The heterogeneous semantic symbols and bit sequences are superposed and separated with the proposed SyD-aware SC technique (SyDSC) and SyD-aware SIC technique (SyDSIC), respectively. Simulation results demonstrate the advantages of the proposed frameworks in improving the communication efficiency of both SUs and BUs, compared with OMA and NOMA-aided transmission benchmarks.

A multi-user heterogeneous semantic communication (SemCom) and bit communication (BitCom) system is investigated. Each user can be served via either SemCom or BitCom for demanding semantic or bit data. Orthogonal/non-orthogonal multiple access (OMA/NOMA) techniques are employed to provide access for multiple users. Channel-based and user demand-based transmission protocols are proposed, where a joint optimization problem of the communication mode selection, frequency bandwidth and power allocation, and NOMA user pairing is formulated to maximize the long-term (equivalent) semantic throughput and user satisfaction, respectively. To solve the formulated problems: 1) For channel-based transmission, a twin-delayed deep deterministic policy gradient with reference neuron enhanced Softmax (TD3-RNS) algorithm is proposed, where a fixed-value neuron is invoked to improve the training efficiency; 2) For user demand-based transmission, a transfer TD3-RNS (T2D3-RNS) algorithm is proposed, where the learned policy is transferred to address the sparse rewards and perverse incentive problem caused by the optimization objective and reward shaping, respectively. Simulation results demonstrate that: i) The proposed heterogeneous scheme outperforms the baselines which merely use SemCom or BitCom; ii) Compared to OMA, NOMA is more compatible with the proposed heterogeneous scheme; and iii) The proposed algorithms outperform the benchmarks in channel-based and user demand-based transmission, respectively.

Integrated Sensing and Communications (ISAC) surpasses the conventional frequency-division sensing and communications (FDSAC) in terms of spectrum, energy, and hardware efficiency, with potential for greater enhancement through integration of non-orthogonal multiple access (NOMA). Leveraging these advantages, a multiple-input multiple-output NOMA-ISAC framework is proposed in this paper, in which the technique of signal alignment is adopted. The performance of the proposed framework for both downlink and uplink is analyzed. 1) The downlink ISAC is investigated under three different precoding designs: a sensing-centric (S-C) design, a communications-centric (C-C) design, and a Pareto optimal design. 2) For the uplink case, two scenarios are investigated: a S-C design and a C-C design, which vary based on the order of interference cancellation between the communication and sensing signals. In each of these scenarios, key performance metrics including sensing rate (SR), communication rate (CR), and outage probability are investigated. For a deeper understanding, the asymptotic performance of the system in the high signal-to-noise ratio (SNR) region is also explored, with a focus on the high-SNR slope and diversity order. Finally, the SR-CR rate regions achieved by ISAC and FDSAC are studied. Numerical results reveal that in both downlink and uplink cases, ISAC outperforms FDSAC in terms of sensing and communications performance and is capable of achieving a broader rate region, clearly showcasing its superiority.

A novel Caching-at-STARS structure, where dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel condition. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. We conceive a cooperative twin delayed deep deterministic policy gradient & deep-Q network (TD3-DQN) algorithm comprised by TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) STARS outperforms RIS significantly in edge caching systems; 3) The proposed cooperative TD3-DQN algorithms is superior in reducing network power consumption than conventional TD3.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.

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.

This paper analyzes the performance of a downlink integrated sensing and communications (ISAC) system, where nonorthogonal multiple access (NOMA) is exploited to mitigate inter-user interference. Closed-form expressions are derived to evaluate the outage probability, ergodic communication rate, and sensing rate. Furthermore, asymptotic analyses are carried out to unveil diversity orders and high signal-to-noise ratio (SNR) slopes of the considered NOMA-ISAC system. As the further advance, the achievable sensing-communication rate region of ISAC is characterized. It is proved that ISAC system is capable of achieving a larger rate region than the conventional frequency-division sensing and communications (FDSAC) system.

A wideband simultaneously transmitting and reflecting surface (STARS) aided terahertz (THz) communication system is proposed. The spatial wideband effect at the base station (BS) and STARS leads to significant performance degradation due to the beam split issue. To address this, true time delayers (TTDs) are introduced into the conventional hybrid beamforming structure for facilitating wideband beamforming. The hybrid beamforming at the BS and the passive beamforming at the STARS are jointly designed to maximize the spectral efficiency of the proposed system. A double-loop iterative algorithm based on penalty dual decomposition is proposed to solve the resulting optimization problem. Finally, the numerical results confirm the effectiveness of exploiting STARS in wideband THz systems.

A near-field wideband multi-user communication system is studied. To eliminate the near-field beam split caused by the wideband spatial effect, a hybrid beamforming architecture based on true-time delayers (TTDs) is exploited. A heuristic two-stage approach is proposed for optimizing the analog and digital beamformers of the TTD-based hybrid beamforming architecture to facilitate near-field wideband beamfocusing. In particular, in the first stage, a closed-form analog beamformer design based on a piecewise-near-field approximation is proposed to maximize the array gain at users. Next, in the second stage, the digital beamformers are optimized by exploiting the successive convex approximation. Finally, our numerical results demonstrate that the proposed approach can effectively eliminate the near-field beam split and outperforms the existing approach in terms of spectral efficiency.

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.

An analytical framework for physical layer security in simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) transmissions is proposed, where legitimate users and eavesdroppers are randomly deployed. To characterize system performance, the channel statistics are first provided, and the Gamma approximation is adopted for general cascaded$\kappa-\mu$fading. Afterwards, the energy splitting (ES) protocol is considered and closed-form expressions of average secrecy capacity are derived. To obtain further insights, the asymptotic secrecy slope is deduced. The theoretical results show that the secrecy slope of the ES protocol is one. The numerical results demonstrate that: 1) there is an optimal resource allocation ratio of STAR-RIS to maximize the system performance; 2) the STAR-RIS-aided NOMA significantly outperforms the STAR-RIS-aided orthogonal multiple access.

In the orthogonal frequency division multiplexing with index modulation (OFDM-IM) framework, we propose an enhanced non-orthogonal multiple access (NOMA) scheme called NOMA with informative envelope (NOMA-IE) to explore the flexibility of signal envelope. In this scheme, data bits are conveyed by the quantified signal envelope in addition to classic signal constellations. Subcarrier activation patterns of different users are jointly decided by the envelope former at the transmitter. At the receiver, successive interference cancellation (SIC) is employed, and we introduce the envelope detection coefficient to eliminate the detection error floor. Considering binary phase shift keying, we derive the asymptotic bit error rate (BER). Analytical results reveal that the imperfect SIC is the main factor degrading the error performance. Numerical results demonstrate the superiority of the NOMA-IE over the OFDM and OFDM-NOMA.

A simultaneously transmitting and reflecting RIS (STAR-RIS) empowered integrated sensing and communications (ISAC) framework is proposed, where the STAR-RIS establishes an additional link to compensate for the insufficient LoS link. To alleviate the conflicts between the limited wireless resources and the multifunctionality requirements, a cluster-based NOMA transmission scheme is adopted, where the communication functionality is employed by the joint communication and sensing (C&S) beam in a NOMA approach. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design. We propose a block coordinate descent (BCD) based iterative algorithm, which splits the optimization variables into two blocks. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed. For the passive BF block, the penalty-based method is invoked to deal with the non-convex constraints. Simulation results verified that our proposed algorithm achieves higher beampattern gain at the intended targets than the other baselines accompanying the least mismatch error.

The novel concept of near-field non-orthogonal multiple access (NF-NOMA) communications is proposed. By exploiting the analog beamformers focusing on specific locations, the far-to-near successive interference cancellation order can be further facilitated. In the proposed NF-NOMA, the two NOMA users in different angular directions with distinct quality of service (QoS) requirements can be grouped into one cluster and are served by one analog beamformer focusing on multiple locations. To maximize the sum rate of higher QoS (H-QoS) users, the analog beamformer is first designed using the beam-splitting technique, which focuses the energy on both two NOMA users at two different locations. Then, a singular value decomposition based zero-forcing (SVD-ZF) digital beamformer is designed to mitigate the inter-cluster interference. Furthermore, an antenna allocation algorithm is proposed by employing the many-to-one matching method. Finally, an iterative algorithm is proposed to obtain suboptimal power allocation solutions via the fractional programming. Numerical results demonstrate that: i) in contrast to the conventional far-field NOMA, the proposed NF-NOMA schemes can achieve a higher spectral efficiency even if the HQoS users are far located; and ii) NF-NOMA transmission always outperforms near-field orthogonal multiple access transmission.

The coexistence of semantic communication (SemCom) and bit-based communication (BitCom) towards next-generation wireless networks is investigated. First, a semantic and bit uplink communication framework is proposed, where a near user (N-user) and a far user (F-user) upload information to the access point employing BitCom and SemCom, respectively. For effectively accommodating the two different users, orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes are proposed. The semantic-versus-bit rate region achieved by each scheme is characterized. The analytical performance comparison shows that NOMA is always superior to OMA, but for the F-user, employing SemCom may not always outperform BitCom. The presented numerical examples verify the analytical result.

The conventional model aggregation-based federated learning (FL) approaches require all local models to have the same architecture and fail to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over-million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. The KFL allows devices to design their machine learning models independently and reduces the communication overhead in the training process. We then experimentally show that different temporal device scheduling patterns lead to considerably different learning performance. With this insight, we formulate a stochastic optimization problem for joint device scheduling and bandwidth allocation under limited devices' energy budgets and develop an efficient online algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on the CIFAR-10 dataset show that the proposed KFL can reduce over 87% communication overhead while achieving better learning performance than the baselines. In addition, the proposed device scheduling algorithm converges faster than benchmark scheduling schemes.

A novel opportunistic semantic and bit communication strategy is proposed for uplink non-orthogonal multiple access (NOMA). Specifically, a secondary far user (F-user) employs either semantic communication (SemCom) or bit-based communication (BitCom) to participate in NOMA with a primary near user (N-user) employing the BitCom. For each fading channel state, the secondary F-user has to select the most suitable communication method, thus striking a good tradeoff between its own achieved performance and the interference imposed on the primary N-user. The optimal communication policy at the F-user over fading channels is derived for maximizing the ergodic (equivalent) semantic rate achieved at the F-user, subject to the minimum ergodic bit rate constraint of the N-user. Numerical results show that the proposed opportunistic scheme can achieve higher communication performance for NOMA than the baseline schemes merely employing SemCom or BitCom. In addition, SemCom can better guarantee the performance of the F-user admitted in NOMA than BitCom when the communication requirement of the primary N-user is high.

An adaptive next generation multiple access (NGMA) downlink scheme is provided, where non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) users are served with the same orthogonal time and frequency resource to address the energy constraints and massive connectivity issues of Internet-of-Things networks. Based on this scheme, the long-term power-constrained sum rate maximization problem is investigated, where beamforming, power allocation, and user clustering are jointly optimized, subject to a long-term total power constraint. To solve the formulated problem, a spatial correlation-based user clustering approach is proposed and a resource allocation algorithm is designed based on the trust region policy optimization (TRPO) algorithm, which demonstrates stable convergence under large learning rates. Numerical results verify that the sum rate of the proposed NGMA scheme outperforms the conventional NOMA and SDMA schemes. Moreover, the spatial correlation-based clustering algorithm achieves an increasing sum rate gain compared to the channel correlation-based baseline algorithm as the spatial correlation in the channel model increases.

Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. However, this task was seldom explored. In this work, based on IPMT, a state-of-the-art few-shot image segmentation method that combines external support guidance information with adaptive query guidance cues, we propose to leverage multi-grained temporal guidance information for handling the temporal correlation nature of video data. We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively. Frame prototypes are further used for each frame independently to handle fine-grained adaptive guidance and enable bidirectional clip-frame prototype communication. To reduce the influence of noisy memory, we propose to leverage the structural similarity relation among different predicted regions and the support for selecting reliable memory frames. Furthermore, a new segmentation loss is also proposed to enhance the category discriminability of the learned prototypes. Experimental results demonstrate that our proposed video IPMT model significantly outperforms previous models on two benchmark datasets. Code is available at https://github.com/nankepan/VIPMT.

How to tackle the unreliability in wireless channels is critical for federated learning (FL). To solve this problem, we propose a novel FL framework, namely FL with gradient recycling (FL-GR), which recycles the historical gradients of unscheduled and transmission-failure devices to improve the learning performance of FL. Based on the proposed FL-GR, we theoretically analyze how the wireless network parameters affect the convergence bound of FL-GR, revealing that scheduling devices with large staleness and increasing their transmit power in each round helps improve learning performance. Simulation results on MNIST and CIFAR-10 show that FL-GR is able to achieve higher accuracy and fast convergence speed than conventional FL algorithms without gradient recycling.

This paper investigates the performance of active reconfigurable intelligent surface (ARIS) assisted non-orthogonal multiple access (NOMA) networks over cascaded Nakagami-m fading channels. The effects of hardware impairments (HIS) and the number of reflection elements on ARIS-NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) are considered. More specifically, we derive the expressions of outage probability with ipSIC/pSIC for ARIS-NOMA-HIS networks. According to the approximated analyses, the diversity orders and high signal-tonoise ratio (SNR) slopes for a pair of non-orthogonal users are attained in detail. The simulation results are presented to verify that the outage behaviors of ARIS-NOMA-HIS networks precede that of ARIS-aided orthogonal multiple access (OMA), passive reconfigurable intelligent surface (PRIS) aided OMA, and other conventional cooperative communication.

A large reconfigurable intelligent surface (LRIS) assisted wireless communication system is investigated in this paper. The increased aperture size and reconfigurable element number of LRIS bring new challenges, including limited incident beam coverage on LRIS, near-field signal propagation, and high beamforming complexity. Against these challenges, a two-step low-complexity beamforming approach is proposed, where a deep reinforcement learning (DRL) algorithm is invoked for determining the optimal beam direction, and a codebook based on the geometric channel state information is designed to map the direction to the beamforming matrixes. The proposed approach not only reduces the computational complexity, but also exploits the geometric channel of BS-LRIS to reduce the channel estimation complexity caused by LRIS. Simulation results indicate that the LRIS can further reduce power consumption compared to the small-size RIS. Meanwhile, the proposed joint codebook-DRL approach achieves a counterbalance compared to the sheer DRL algorithm with lower complexity.

Coverage and capacity are the important metrics for performance evaluation in wireless networks, while the coverage and capacity have several conflicting relationships, e.g. high transmit power contributes to large coverage but high inter-cell interference reduces the capacity performance. Therefore, in order to strike a balance between the coverage and capacity, a novel model is proposed for the coverage and capacity optimization of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) assisted networks. To solve the coverage and capacity optimization (CCO) problem, a machine learning-based multi-objective optimization algorithm, i.e., the multi-objective proximal policy optimization (MO-PPO) algorithm, is proposed. In this algorithm, a loss function-based update strategy is the core point, which is able to calculate weights for both loss functions of coverage and capacity by a min-norm solver at each update. The numerical results demonstrate that the investigated update strategy outperforms the fixed weight-based MO algorithms.

A reconfigurable intelligent surface (RIS)-assisted downlink nonorthogonal multiple access (NOMA) Internet of Things (IoT) network is proposed, where a Quality-of-Service (QoS)-based NOMA clustering scheme is conceived to effectively utilize the limited wireless resources among IoT devices. A throughput maximization problem is formulated by jointly optimizing the phase shifts of the RIS and the power allocation of the base station (BS) from the short-term and long-term perspectives. We aim to investigate and compare the performance of deep learning (DL) and deep reinforcement learning (DRL) algorithms for solving the formulated problems. In particular, the DL method utilizes model-agnostic-metalearning (MAML) to enhance the generalization capability of the neural network and to accelerate the convergence rate. For the DRL method, the deep deterministic policy gradient (DDPG) algorithm is employed to incorporate continuous phase-shift variables. It shows that the DL method only focuses on the maximization of the instantaneous throughput, whereas the DRL method can coordinate the power consumption over different time slots to maximize the long-term throughput. Numerical results demonstrate that: 1) the proposed QoS-based NOMA clustering scheme achieves higher IoT throughput than the conventional channel-based scheme; 2) the implementation of RISs induces approximately 5%–25% throughput gain as the number of RIS elements increases from 8 to 64; 3) DL and DRL achieve a similar throughput performance for the short-term optimization, while DRL is superior for the long-term optimization, especially when the total transmit power is limited.

A novel semantics-empowered two-user uplink non-orthogonal multiple access (NOMA) framework is proposed for resource efficiency enhancement. More particularly, a secondary far user (F-user) employs the semantic communication (SemCom) while a primary near user (N-user) employs the conventional bit-based communication (BitCom). The fundamental performance limit, namely semantic-versus-bit (SvB) rate region, of the proposed semantics-empowered NOMA framework is characterized. The equivalent SvB rate region achieved by the conventional BitCom-based NOMA is provided as the baseline scheme. It unveils that, compared to BitCom, SemCom can significantly improve the F-user’s performance when its permitted transmit power is strictly capped, but may perform worse when its permitted transmit power is high. Guided by this result, the proposed semantics-empowered NOMA framework is investigated over fading channels. An opportunistic SemCom and BitCom scheme is proposed, which enables the secondary F-user to participate in NOMA via the most suitable communication method at each fading state, thus striking a good tradeoff between its own achieved performance and the interference imposed on the primary N-user. Two scenarios are considered for employing the opportunistic scheme, namely on-off resource management and continuous resource management. For each scenario, the optimal communication policy over fading channels is derived for maximizing the ergodic semantic rate achieved at the secondary F-user, subject to the minimum ergodic bit rate constraint of the primary N-user. Numerical results show that: 1) proposed opportunistic scheme in both scenarios can achieve higher communication performance for NOMA than the baseline schemes merely employing SemCom or BitCom; 2) SemCom can better guarantee the performance of the F-user admitted in NOMA than BitCom when the communication requirement of the primary N-user is high; and 3) continuous power control at the F-user is necessary for ensuring high performance over fading channels, while the on-off time scheduling is sufficient.

Multiple access (MA) design is investigated to facilitate the coexistence of the emerging semantic transmission and the conventional bit-based transmission in future networks. Thesemantic rateis adopted for measuring the performance of the semantic transmission. However, a key challenge is that there is no closed-form expression for a key parameter, namely thesemantic similarity, which characterizes the sentence similarity between an original sentence and the corresponding recovered sentence. To overcome this challenge, we propose a data regression method, where the semantic similarity is approximated by ageneralized logistic function. Using the obtained tractable function, we propose a heterogeneous semantic and bit communication framework, where an access point simultaneously sends the semantic and bit streams to one semantics-interested user (S-user) and one bit-interested user (B-user). To realize this heterogeneous semantic and bit transmission in multi-user networks, three MA schemes are proposed, namely orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and semi-NOMA. More specifically, the bit stream in semi-NOMA is split into two streams, one is transmitted with the semantic stream over the shared frequency sub-band and the other is transmitted over the separate orthogonal frequency sub-band. To study the fundamental performance limits of the three proposed MA schemes, thesemantic-versus-bit (SvB) rate regionand thepower regionare defined. An optimal resource allocation procedure is then derived for characterizing the boundary of the SvB rate region and the power region achieved by each MA scheme. The structures of the derived solutions demonstrate that semi-NOMA is superior to both NOMA and OMA given its highly flexible transmission policy. Our numerical results: 1) confirm that the proposed semi-NOMA is the optimal MA scheme as compared to OMA and NOMA even under the symmetric channel case, and 2) reveal that the superiority of semi-NOMA is more prominent when the channel condition of the S-user is better than that of the B-user.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated, which can maximize the computation offloading capacity and suppress inter-functionality interference. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. Both partial and binary computation offloading modes are considered: 1) For the partial offloading mode, a weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. It is proved that a Karush–Kuhn–Tucker optimal solution can be obtained; 2) For the binary offloading mode, the resultant highly-coupled mixed-integer optimization problem is first transformed to an equivalent but more tractable form. Then, the reformulated problem is solved by utilizing the alternating direction method of multipliers approach to obtain a nearly optimal solution. Finally, numerical results verify the effectiveness of the proposed algorithms and reveal that: i) the computation rate can be significantly enhanced by exploiting the multi-tier computing architecture when the BS is resource-limited, and ii) the proposed NOMA-aided JSCMC framework is superior in inter-functionality interference management and can achieve high-quality sensing and computing performance simultaneously compared with other benchmark schemes.

A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rate while satisfying the SIC decoding requirements and users’ minimum data rate requirements. To address this highly complex and coupling non-convex mixed integer nonlinear programming (MINLP), a novel distributed auto-learning graph neural network (AutoGNN) architecture is proposed to alleviate the overwhelming information exchange burdens among base stations (BSs). The proposed AutoGNN can train the GNN model weights whilst automatically optimizing the GNN architecture, namely the GNN network depth and message embedding sizes, to achieve communication-efficient distributed scheduling. Based on the proposed architecture, a bi-level AutoGNN learning algorithm is further developed to efficiently approximate the hypergradient in model training. It is theoretically proved that the proposed bi-level AutoGNN learning algorithm can converge to a stationary point. Numerical results reveal that: 1) the proposed cluster-free NOMA framework outperforms the conventional cluster-based NOMA framework in the multi-cell scenario; and 2) the proposed AutoGNN architecture significantly reduces the computation and communication overheads compared to the conventional convex optimization-based methods and the conventional GNNs with fixed architectures.

Abstract Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative categorizing approaches for STAR-RISs are introduced from the phase-shift, directional, and energy consumption perspectives. Furthermore, the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases. As a recent advance, a general optimization framework, which has high compatibility and provable optimality regardless of the application scenarios, is proposed. As a further advance, several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication. Lastly, a few future directions and research opportunities are highlighted.

The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over a million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. This framework allows devices to design their machine-learning models independently and reduces the communication overhead in the training process. We then theoretically analyze the convergence bound of the proposed framework under a non-convex loss function setting, revealing that scheduling more data volume in each round helps to improve the learning performance. In addition, large data volume should be scheduled in early rounds if the total scheduled data volume during the entire learning course is fixed. Inspired by this, we define a new objective function, i.e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control. To deal with unknown time-varying wireless channels, we transform the considered problem into a deterministic problem for each round with the assistance of the Lyapunov optimization framework. Then, we derive the optimal bandwidth allocation and power control solution by convex optimization techniques. We also develop an efficient online device scheduling algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on two typical datasets (i.e., MNIST and CIFAR-10) under highly heterogeneous local data distributions show that the proposed KFL is capable of reducing over 99% communication overhead while achieving better learning performance than the conventional model aggregation-based algorithms. In addition, the proposed device scheduling algorithm converges faster than the benchmark scheduling schemes.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

Metaverse aims for building a fully immersive virtual shared space, where the users are able to engage in various activities. To successfully deploy the service for each user, the Metaverse service provider and network service provider generally localise the user first and then support the communication between the base station (BS) and the user. A reconfigurable intelligent surface (RIS) is capable of creating a reflected link between the BS and the user to enhance line-of-sight. Furthermore, the new key performance indicators (KPIs) in Metaverse, such as its energy-consumption-dependent total service cost and transmission latency, are often overlooked in ultra-reliable low latency communication (URLLC) designs, which have to be carefully considered in next-generation URLLC (xURLLC) regimes. In this paper, our design objective is to jointly optimise the transmit power, the RIS phase shifts, and the decoding error probability to simultaneously minimise the total service cost and transmission latency and approach the Pareto Front (PF). We conceive a twin-stage central controller, which aims for localising the users first and then supports the communication between the BS and users. In the first stage, we localise the Metaverse users, where the stochastic gradient descent (SGD) algorithm is invoked for accurate user localisation. In the second stage, a meta-learning-based position-dependent multi-objective soft actor and critic (MO-SAC) algorithm is proposed to approach the PF between the total service cost and transmission latency and to further optimise the latency-dependent reliability. Our numerical results demonstrate that 1) The proposed solution strikes a tradeoff between the total service cost and transmission latency, which provides a candidate group of optimal solutions for diverse practical scenarios. 2) The proposed meta-learning-based MO-SAC algorithm is capable of adaption to new wireless environments, compared to the benchmarkers. 3) The approximate PF depicted discovered the relationships among the KPIs for the Metaverse, which provides guidelines for its deployment.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) is a promising passive device that contributes to full-space coverage via transmitting and reflecting the incident signal simultaneously. As a new paradigm in wireless communications, how to analyze the coverage and capacity performance of STAR-RISs becomes essential but challenging. To solve the coverage and capacity optimization (CCO) problem in STAR-RIS-assisted networks, a multi-objective proximal policy optimization (MO-PPO) algorithm is proposed to handle long-term effects. To strike a balance between each objective, the MO-PPO algorithm provides a set of optimal solutions to approach a Pareto front (PF), where the solution on the approximate PF is regarded as an optimal result. Moreover, in order to improve the performance of the MO-PPO algorithm, two update strategies, i.e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS), are investigated. For the AVUS, the improved point is to integrate the action values of both coverage and capacity and then update the loss function. For the LFUS, the improved point is only to assign dynamic weights for both loss functions of coverage and capacity, while the weights are calculated by a min-norm solver at every update. The numerical results demonstrated that the investigated update strategies outperform the fixed weights MO optimization algorithms in different cases, which include a different number of sample grids, the number of STAR-RISs, the number of elements in the STAR-RISs, and the size of STAR-RISs. Additionally, the STAR-RIS-assisted networks achieve better performance than conventional wireless networks without STAR-RISs. Moreover, with the same bandwidth, a millimetre wave is able to provide higher capacity than sub-6 GHz, but at a cost of smaller coverage.

Reconfigurable intelligent surfaces (RIS) are capable of beneficially ameliorating the propagation environment by appropriately controlling the passive reflecting elements. To extend the coverage area, the concept of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has been proposed, yielding supporting 360° coverage user equipment (UE) located on both sides of the RIS. In this paper, we theoretically formulate the ergodic sum-rate of the STAR-RIS assisted non-orthogonal multiple access (NOMA) uplink in the face of channel estimation errors and hardware impairments (HWI). Specifically, the STAR-RIS phase shift is configured based on the statistical channel state information (CSI), followed by linear minimum mean square error (LMMSE) channel estimation of the equivalent channel spanning from the UEs to the access point (AP). Afterwards, successive interference cancellation (SIC) is employed at the AP using the estimated instantaneous CSI, and we derive the theoretical ergodic sum-rate upper bound for both perfect and imperfect SIC decoding algorithm. The theoretical analysis and the simulation results show that both the channel estimation and the ergodic sum-rate have performance floor at high transmit power region caused by transceiver hardware impairments.

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.

A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC). In contrast to conventional NOMA approaches, where SIC is successively carried out within the same cluster, the key idea is that the SIC can be flexibly implemented between any arbitrary users to achieve efficient interference elimination. Based on the proposed framework, a sum rate maximization problem is formulated for jointly optimizing the transmit beamforming and the SIC operations between users, subject to the SIC decoding conditions and users’ minimal data rate requirements. To tackle this highly-coupled mixed-integer nonlinear programming problem, an alternating direction method of multipliers-successive convex approximation (ADMM-SCA) algorithm is developed. The original problem is first reformulated into a tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex terms via SCA. Then, this AL problem is decomposed into two subproblems that are iteratively solved by the ADMM to obtain the stationary solution. Furthermore, to reduce the computational complexity and alleviate the parameter initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed. The intractable binary SIC operations are solved through an extended many-to-many matching, which is jointly combined with an SCA process to optimize the transmit beamforming. The proposed Matching-SCA can converge to an enhanced exchange-stable matching that guarantees the local optimality. Numerical results demonstrate that: i) the proposed Matching-SCA algorithm achieves comparable performance and a faster convergence compared to ADMM-SCA; ii) the proposed generalized framework realizes scenario-adaptive communications and outperforms traditional multi-antenna NOMA approaches in various communication regimes.

The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs). However, the conventional RISs require that the WDs have to be located in the half-plane of the reflection space, which restricts their potential benefits. To address this issue, the novel family of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is considered in AirComp systems to improve the computation accuracy across a wide coverage area. To minimize the computation mean-squared-error (MSE) in STAR-RIS assisted AirComp systems, we propose a joint beamforming design for optimizing both the transmit power at the WDs, as well as the passive reflect and transmit beamforming matrices at the STAR-RIS, and the receive beamforming vector at the fusion center (FC). Specifically, in the updates of the passive reflect and transmit beamforming matrices, closed-form solutions are derived by introducing an auxiliary variable and exploiting the coupled binary phase-shift conditions. Moreover, by assuming that the number of antennas at the FC and that of elements at the STAR-RIS/RIS are sufficiently high, we theoretically prove that the STAR-RIS assisted AirComp systems provide higher computation accuracy than the conventional RIS assisted systems. Our numerical results show that the proposed beamforming design outperforms the benchmark schemes relying on random phase-shift constraints and the deployment of conventional RIS. Moreover, its performance is close to the lower bound achieved by the beamforming design based on the STAR-RIS dispensing with coupled phase-shift constraints.

The new concept of semi-integrated-sensing-and-communication (Semi-ISaC) is proposed for next-generation cellular networks. Compared to the state-of-the-art, where the total bandwidth is used for integrated sensing and communication (ISaC), the proposed Semi-ISaC framework provides more freedom as it allows that a portion of the bandwidth is exclusively used for either wireless communication or radar detection, while the rest is for ISaC transmission. To enhance the bandwidth efficiency (BE), we investigate the evolution of Semi-ISaC networks from orthogonal multiple access (OMA) to non-orthogonal multiple access (NOMA). First, we evaluate the performance of an OMA-based Semi-ISaC network. As for the communication signals, we investigate both the outage probability (OP) and the ergodic rate. As for the radar echoes, we characterize the ergodic radar estimation information rate (REIR). Then, we investigate the performance of a NOMA-based Semi-ISaC network, including the OP and the ergodic rate for communication signals and the ergodic REIR for radar echoes. The diversity gains of OP and the high signal-to-noise ratio (SNR) slopes of the ergodic REIR are also evaluated as insights. The analytical results indicate that: 1) Under a two-user NOMA-based Semi-ISaC scenario, the diversity order of the near-user is equal to the coefficient of the Nakagami-${m}$fading channels ($m$), while that of the far-user is zero; and 2) The high-SNR slope for the ergodic REIR is based on the ratio of the radar signal’s duty cycle to the pulse duration. Our simulation results show that: 1) Semi-ISaC has better channel capacity than the conventional ISaC; and 2) The NOMA-based Semi-ISaC has better channel capacity than the OMA-based Semi-ISaC.

A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network ($\text{D}^{3}$QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots’ trajectories,$\text{D}^{3}$QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed$\text{D}^{3}$QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.

A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate maximization and max-min rate fairness problems are formulated for jointly optimizing the power allocation at the access point and relay as well as the passive beamforming design at the RIS. To tackle the non-convex problems, an alternating optimization (AO) based algorithm is first developed, where the transmit power and the RIS phase-shift are alternatingly optimized by leveraging the two-dimensional search and rank-relaxed difference-of-convex (DC) programming, respectively. Then, a two-layer penalty based joint optimization (JO) algorithm is developed to jointly optimize the resource allocation coefficients within each iteration. Finally, numerical results demonstrate that: i) the proposed coexisting RIS and relay assisted transmission framework is capable of achieving a significant user performance improvement than conventional schemes without RIS or relay; ii) compared with the AO algorithm, the JO algorithm requires less execution time at the cost of a slight performance loss; and iii) the H-NOMA and F-NOMA protocols are generally preferable for ensuring user rate fairness and enhancing user sum rate, respectively.

A simultaneously transmitting and reflecting surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the entire space is partitioned by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure is proposed, where dedicated sensors are mounted at STARS to address the significant path loss and clutter interference of sensing. The Cramér-Rao bound (CRB) of the two-dimensional (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Both independent and coupled phase-shift models of STARS are investigated: 1) For the independent phase-shift model, to address the coupling problem of ISAC waveform and STARS coefficient, an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) framework is conceived; 2) For the coupled phase-shift model, based on the PDD framework, a low complexity alternating optimization algorithm is proposed to tackle the coupled phase-shift constraint by alternately optimizing the amplitude and phase-shift coefficients of STARS with closed-form expressions. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms conventional RIS in terms of CRB under the communication constraints; 2) coupled phase-shift model achieves comparable performance to the independent one for low communication requirements or sufficient STARS elements; 3) it is more efficient to increase the number of passive elements of STARS than the active elements of the sensor; 4) higher sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.

Simultaneously transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS) has been introduced to achieve full coverage area. This paper investigate the performance of STAR-RIS assisted non-orthogonal multiple access (NOMA) networks over Rician fading channels, where the incidence signals sent by base station are reflected and transmitted to the nearby user and distant user, respectively. To evaluate the performance of STAR-RIS-NOMA networks, we derive new approximate expressions of outage probability and ergodic rate for a pair of users, in which the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into consideration. Based on the asymptotic expressions, the diversity orders of the nearby user with ipSIC/pSIC and distant user are achieved carefully. The high signal-to-noise ratio slopes of ergodic rates for nearby user with pSIC and distant user are equal to $one$ and $zero$, respectively. In addition, the system throughput of STAR-RIS-NOMA is discussed in delay-limited and delay-tolerant modes. Simulation results are provided to verify the accuracy of the theoretical analyses and demonstrate that: 1) The outage probability of STAR-RIS-NOMA outperforms that of STAR-RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication systems; 2) With the increasing of reflecting elements $K$ and Rician factor $\kappa $, the STAR-RIS-NOMA networks are capable of attaining the enhanced performance; and 3) The ergodic rates of STAR-RIS-NOMA are superior to that of STAR-RIS-OMA.

Different from traditional reflection-only reconfigurable intelligent surfaces (RISs), simultaneously transmitting and reflecting RISs (STAR-RISs) represent a novel technology, which extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. STAR-RISs provide new degrees-of-freedom (DoF) for manipulating signal propagation. Motivated by the above, a novel STAR-RIS assisted non-orthogonal multiple access (NOMA) (STAR-RIS-NOMA) system is proposed in this paper. Our objective is to maximize the achievable sum rate by jointly optimizing the decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming. However, the formulated problem is non-convex with intricately coupled variables. To tackle this challenge, a suboptimal two-layer iterative algorithm is proposed. Specifically, in the inner-layer iteration, for a given decoding order, the power allocation coefficients, active beamforming, transmission and reflection beamforming are optimized alternatingly. For the outer-layer iteration, the decoding order of NOMA users in each cluster is updated with the solutions obtained from the inner-layer iteration. Moreover, an efficient decoding order determination scheme is proposed based on the equivalent-combined channel gains. Simulation results are provided to demonstrate that the proposed STAR-RIS-NOMA system, aided by our proposed algorithm, outperforms conventional RIS-NOMA and RIS assisted orthogonal multiple access (RIS-OMA) systems.

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5iand COCO-20idatasets show that our approach is effective despite its simplicity. Code is available at https://github.com/LIUYUANWEI98/NERTNet

Semi-grant-free non-orthogonal multiple access (SGF-NOMA) is a potential paradigm to support massive connec-tivity for the short packets Internet of things (IoT) applications while satisfying the undistracted transmission requirements of primary IoT users. However, resource allocation in SGF-NOMA is more challenging due to the sporadic traffic of grant-free (GF) users and the need to satisfy the quality of service (QoS) requirements of grant-based (GB) users. The GF users access and choose resources at random, resulting in frequent power collisions and decoding failures at the base station (BS). This paper develops a general learning framework that enables GF users to learn from historical information to avoid power collisions. We utilize a hybrid multi-agent deep reinforcement learning (hMA-DRL) framework to maximize the connectivity and enhance the number of successful decoded users at the BS. The numerical results show that the proposed scheme achieves a solution near to the optimal one and increases the successful decoded users by 42.38% as compared to the benchmark scheme. The considered algorithm performs well with an increasing number of users as compared to the competitive and cooperative MA-DRL algorithms.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. A weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. Fi-nally, numerical results show the significant performance gain achieved by the proposed schemes over the benchmark schemes.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have received great attention due to the capability of achieving full-space coverage. In this paper, the queue-stable STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure the stability of queueing systems. To tackle the challenge of infinite time slots required for stability, the long-term stability-oriented problem is reformulated to maximize the per-slot queue-weighted sum rate (QWSR) of users. In particular, the rate weight allocated for each user is determined by the length of a data queue, which is maintained at the base station (BS) and pending to be delivered to each user. Then, the QWSR is maximized by jointly optimizing the NOMA decoding order, the active beamforming coefficients (ABCs) at the BS, and the passive transmission and reflection coefficients (PTRCs) at the STAR-RIS. To handle the highly-coupled and non-convex problem, the blocked coordinate descent and the successive convex approximation methods are invoked to iteratively and alternatively optimize the problem. Simulation results revel that: i) our proposed STAR-RIS assisted NOMA communication achieves better performance than the conventional schemes; ii) the reformulated per-slot QWSR maximization problem is proven to ensure the system stability.

In this paper, we propose a novel distributed resource allocation mechanism for semi-grant-free non-orthogonal multiple access (SGF-NOMA) transmission to maximize the network throughput, where multi-agent deep reinforcement learning with prioritized experience replay (PER) is employed. We design a centralized training framework and decentralized decision making to increase the flexibility of the proposed scheme. More specifically, each grant-free user as an "agent" learns the dynamics of the environment and makes its decisions independently in a decentralized manner. No heavy information exchange is needed to find the optimal transmit power and sub-channel that maximize the throughput. Numerical results show that the proposed algorithm with PER enhances the learning efficiency compared to the algorithm with conventional replay buffer and outperforms the existing scheme with a 12% throughput increase.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated, where an access point sends information to two users located on each side of the STAR-RIS. Different from current works assuming that the phase-shift coefficients for transmission and reflection can be independently adjusted, which is non-trivial to realize for purely passive STAR-RISs, a coupled transmission and reflection phase-shift model is considered. Based on this model, a power consumption minimization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). In particular, the amplitude and phase-shift coefficients for transmission and reflection are jointly optimized, subject to the rate constraints of the users. To solve this non-convex problem, an efficient element-wise alternating optimization algorithm is developed to find a high-quality suboptimal solution, whose complexity scales only linearly with the number of STAR elements. Finally, numerical results are provided for both NOMA and OMA to validate the effectiveness of the proposed algorithm by comparing its performance with that of STAR-RISs using the independent phase-shift model and conventional reflecting/transmitting-only RISs.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric user (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. A beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed scheme over the benchmark schemes.

This paper proposes a novel semi-federated learning (SemiFL) paradigm, which integrates centralized learning (CL) and over-the-air federated learning (AirFL) into a unified framework, with the aid of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, this SemiFL framework allows computing-scarce users to participant in the learning process by using non-orthogonal multiple access (NOMA) to transmit their local dataset to the base station for model computation on behalf of them. During the uplink communication, scarce spectrum resources are shared among AirFL users and NOMA-based CL users, using a STAR-RIS for interference management and coverage enhancement. To analyze the learning behavior of SemiFL, closed-form expressions are derived to quantify the impact of learning rates and noisy fading channels. Our analysis shows that SemiFL can achieve a lower error floor than the CL or AirFL schemes with partial users. Simulation results show that SemiFL significantly reduces communication overhead and latency compared to CL, while achieving better learning performance than AirFL.

A non-orthogonal multiple access (NOMA) inspired integrated sensing and communication (ISAC) system is investigated. A dual-functional base station (BS) serves multiple communication users while sensing multiple targets, by trans-mitting the non-orthogonal superposition of the communication and sensing signals. A NOMA inspired interference cancellation scheme is proposed, where part of the dedicated sensing signal is treated as the virtual communication signals to be mitigated at each communication user via successive interference cancellation (SIC). Based on this framework, the transmitted communication and sensing signals are jointly optimized to match the desired sensing beampattern, while satisfying the minimum rate requirement and the SIC condition at the communication users. Then, the formulated non-convex optimization problem is solved by invoking the successive convex approximation (SCA) to obtain a near-optimal solution. The numerical results show the proposed NOMA-inspired ISAC system can achieve better performance than the conventional ISAC system and comparable performance to the ideal ISAC system where all sensing interference is assumed to be removed unconditionally.

This paper investigates non-orthogonal multiple access (NOMA) assisted integrated sensing and communication (ISaC) networks. Compared to the conventional ISaC networks, where the total bandwidth is used for both the radar detection and wireless communications, the proposed Semi-ISaC networks allow that a portion of bandwidth is used for ISaC and the rest of the bandwidth is only utilized for wireless communications. We first derive the analytical expressions of the outage probability for the communication signals, including the signals for the radar target and the communication transmitter. Additionally, we derive the analytical expressions of the ergodic radar estimation information rate (REIR) for the radar echoes. The simulation results show that 1) NOMA ISaC has better spectrum efficiency than the conventional ISaC; and 2) The REIR is enhanced when we enlarge the density of pulses.

A novel framework of reconfigurable intelligent surfaces (RISs) enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable to provide NOMA with artificially controlled channels, which can be a beneficial condition for NOMA networks. To optimize the sum rate of all users, a federated learning enhanced deep deterministic policy gradient (FL-DDPG) algorithm is proposed to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. Our simulation results indicate that the mobile RIS scheme can provide about three times data rate gain compare to the fixed RIS. Moreover, the NOMA scheme is capable to achieve a significant data rate gain in contrast with the OMA scheme. Finally, the FL-DDPG algorithm has a superior convergence rate and optimization performance than that of the independent training framework.

WiFi-based sensing for human activity recognition (HAR) has recently become a hot topic as it brings great benefits when compared with video-based HAR, such as eliminating the demands of line-of-sight (LOS) and preserving privacy. Making the WiFi signals to ’see’ the action, however, is quite coarse and thus still in its infancy. An end-to-end spatiotemporal WiFi signal neural network (STWNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios. Especially, the 3D convolution module is able to explore the spatiotemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features. In addition, a novel 3D representation for WiFi signals is designed to preserve multi-scale spatiotemporal information. Furthermore, a small wireless-vision dataset (WVAR) is synchronously collected to extend the potential of STWNN to ’see’ through occlusions. Quantitative and qualitative results on WVAR and the other three public benchmark datasets demonstrate the effectiveness of our approach on both accuracy and shift consistency.

WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring due to the pervasive and unobtrusive nature of WiFi signals. However, the efficacy of WiFi signals is prone to be influenced by the change in the ambient environment and varies over different sub-carriers. To remedy this issue, we propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios. In particular, a new Gabor residual block is designed to address the impact of the changing surrounding environment with a focus on learning reliable and robust temporal-frequency representations of WiFi signals. In each block, the Gabor layer is integrated with the anti-aliasing layer in a residual manner to gain the shift-invariant features. Furthermore, fractal temporal and frequency self-attention are proposed in a joint effort to explicitly concentrate on the efficacy of WiFi signals and thus enhance the quality of output features scattered in different subcarriers. Experimental results throughout our wireless-vision action recognition dataset (WVAR) and three public datasets demonstrate that our proposed GraSens scheme outperforms state-of-the-art methods with respect to recognition accuracy.

Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information gap between query and support images. If the objects in them show large intra-class diversity, forcibly migrating the category information from the support to the query is ineffective. To solve this problem, we are the first to introduce an intermediate prototype for mining both deterministic category information from the support and adaptive category knowledge from the query. Specifically, we design an Intermediate Prototype Mining Transformer (IPMT) to learn the prototype in an iterative way. In each IPMT layer, we propagate the object information in both support and query features to the prototype and then use it to activate the query feature map. By conducting this process iteratively, both the intermediate prototype and the query feature can be progressively improved. At last, the final query feature is used to yield precise segmentation prediction. Extensive experiments on both PASCAL-5i and COCO-20i datasets clearly verify the effectiveness of our IPMT and show that it outperforms previous state-of-the-art methods by a large margin. Code is available at https://github.com/LIUYUANWEI98/IPMT

Reconfigurable intelligent surface (RIS) has been regarded as a promising technology since it has ability to create the favorable channel conditions. This paper investigates the secrecy performance of RIS aided non-orthogonal multiple access (NOMA) networks, where the internal eavesdropping scenario is taken into consideration. More specifically, novel closed-form and asymptotic expressions of secrecy outage probability for the k-th user are derived. According to the analytical results, the secrecy diversity orders at users are acquired in the high signal-to-noise ratio region. Simulation results show that the applying of RIS in NOMA networks can remarkably improve the performance of secrecy outage behaviour and secrecy system throughput compared to RIS aided orthogonal multiple access networks.

A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the significant path loss and clutter interference for sensing. The Cramér-Rao bound (CRB) of the 2-dimension (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Moreover, to address the coupled issue in the modified FIM, an efficient double-loop iterative algorithm based on the penalty dual decomposition method is conceived. The numerical results demonstrate that: 1) STARS significantly outperforms the conventional transmitting/reflecting-only intelligent surface; 2) High sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator.

In this work, we focus on simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) with coupled transmission and reflection phase shifts. We demonstrate how to achieve full diversity for users on both sides by proposing a practical phase-shift design, namely, the diversity preserving design. To evaluate the performance, a STAR-RIS-aided two-user downlink communication system is investigated for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA). The outage probabilities, diversity orders, and power scaling laws under this design are studied and compared with the performance upper and lower bounds. Numerical simulations show that the proposed diversity preserving phase-shift configuration strategy for the STAR-RIS achieves the same diversity order as the STAR-RISs assuming independent phase-shift, and achieves a comparable power scaling law with only 4 dB power reduction.

In this paper, we investigate a robust beamforming design in a millimeter wave (mmWave) communication network with consideration of the random blockages. The network with multiple remote radio units (RRUs) is taken into account, where the joint transmission coordinated multi-point (JT-CoMP) scheme is adopted to improve the spectrum efficiency. To further enhance the communication performance and maintain the reliability of the network, an active intelligent reflecting surface (IRS) panel is deployed. We formulate the robust beamforming design of the mmWave communication network as an optimization problem aiming at minimizing the total transmit power at the RRUs subject to the average signal-to-interference-plus-noise ratio (SINR) constraints and power constraint over the active IRS. To deal with the challenge caused by the variables coupling, an algorithm based on the alternating optimization (AO) and semidefinite programming (SDP) is proposed. Numerical results illustrate that: i) the transmit power of the network can be reduced by deploying both the passive and active IRSs; ii) the integration of the active IRS and CoMP scheme can obtain a significant performance gain compared to that of the conventional passive IRS and CoMP.

This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omnidirectional coverage extension. To capture the impact of nonideal wireless channels on AirFL, a closed-form expression for the optimality gap (also known as the convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes, as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with a linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer nonlinear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust-region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled nonconvex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that: 1) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications; 2) our algorithms achieve a faster convergence rate on independent and identically distributed (IID) and non-IID settings compared to the existing baselines; and 3) both the spectrum efficiency and learning performance are significantly improved with the aid of the well-tuned STAR-RIS.

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max–min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV’s trajectory. We develop a concave–convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max–min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate thisdouble spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, abeamformer-basedNOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where acluster-basedNOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed schemes over the benchmark schemes employing conventional transmission strategies.

With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed data rate and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users’ performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namelygraph-embedded value-decomposition actor-critic (GE-VDAC), that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms.

A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.

A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility.

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is capable of providing full-space coverage of smart radio environments. This work investigates STAR-RIS aided downlink non-orthogonal multiple access (NOMA) multi-cell networks, where the energy of incident signals at STAR-RISs is split into two portions for transmitting and reflecting. We first propose a fitting method to model the distribution of composite small-scale fading power as the tractable Gamma distribution. Then, a unified analytical framework based on stochastic geometry is provided to capture the random locations of RIS-RISs, base stations (BSs), and user equipments (UEs). Based on this framework, we derive the coverage probability and ergodic rate of both the typical UE and the connected UE. In particular, we obtain closed-form expressions of the coverage probability in interference-limited scenarios. We also deduce theoretical expressions in conventional RIS aided networks for comparison. The analytical results show that optimal energy splitting coefficients of STAR-RISs exist to simultaneously maximize the system coverage and ergodic rate. The numerical results demonstrate that: 1) STAR-RISs are able to meet different demands of UEs located on different sides; 2) STAR-RISs with appropriate energy splitting coefficients outperform conventional RISs in the coverage and the rate performance.

In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs’ energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs.

This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a base station (BS) serves users securely with the assistance of distributed IRSs. Considering that eavesdropper’s instantaneous channel state information (CSI) is challenging to acquire in practice, we utilize secrecy outage probability (SOP) as the security metric. A problem of maximizing the minimum secrecy rate among users, by jointly optimizing transmit beamforming at the BS and phase shifts of the IRSs, is formulated. For a special case with a single-antenna BS, we derive the closed-form SOP expressions and propose a novelring-penaltybased successive convex approximation (SCA) algorithm to design transmit power and phase shifts jointly. For a general multi-antenna BS case, we develop a Bernstein-type inequality based alternating optimization (AO) algorithm to solve the challenging problem. Numerical results demonstrate the advantages of the proposed algorithms over the baseline schemes. The results also show that: 1) the maximum secrecy rate is achieved when distributed IRSs share the reflecting elements equally; and 2) the distributed IRS deployment does not always outperform the centralized IRS deployment, due to the tradeoff between the number of IRSs and the reflecting elements equipped at each IRS.

With the rapid growth of data traffic in cellular networks, edge caching has become an emerging technology for traffic offloading. We investigate the caching placement and content delivery in cache-enabling cellular networks. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay which considers both content transmission delay and content update delay. To solve this challenging problem, we decompose the optimization problem into two sub-problems, the user association sub-problem in a short time scale and the caching placement in a long time scale. Specifically, we propose a low complexity user association algorithm for a given caching placement in the short time scale. Then we develop a deep deterministic policy gradient based caching placement algorithm which involves the short time-scale user association decisions in the long time scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We illustrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

With the exponential growth of data traffic in wireless networks, edge caching has been regarded as a promising solution to offload data traffic and alleviate backhaul congestion, where the contents can be cached by an unmanned aerial vehicle (UAV) and user terminal (UT) with local data storage. In this article, a cooperative caching architecture of UAV and UTs with scalable video coding (SVC) is proposed, which provides the high transmission rate content delivery and personalized video viewing qualities in hotspot areas. In the proposed cache-enabling UAV-D2D networks, we formulate a joint optimization problem of UT caching placement, UAV trajectory, and UAV caching placement to maximize the cache utility. To solve this challenging mixed integer nonlinear programming problem, the optimization problem is decomposed into three sub-problems. Specifically, we obtain UT caching placement by a many-to-many swap matching algorithm, then obtain the UAV trajectory and UAV caching placement by approximate convex optimization and dynamic programming, respectively. Finally, we propose a low complexity iterative algorithm for the formulated optimization problem to improve the system capacity, fully utilize the cache space resource, and provide diverse delivery qualities for video traffic. Simulation results reveal that: i) the proposed cooperative caching architecture of UAV and UTs obtains larger cache utility than the cache-enabling UAV networks with same data storage capacity and radio resource; ii) compared with the benchmark algorithms, the proposed algorithm improves cache utility and reduces backhaul offloading ratio effectively.

By activating blocked users and altering successive interference cancellation (SIC) sequences, reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems. To evaluate the benefits between RISs and NOMA, a downlink RIS-aided multi-cell-NOMA network is investigated via stochastic geometry. We first introduce the unique path loss model for RIS reflecting channels. Then, we evaluate the angle distributions based on a Poisson cluster process (PCP) model, which theoretically demonstrates that the angles of incidence and reflection are uniformly distributed. Additionally, we derive closed-form analytical and asymptotic expressions for coverage probabilities of the paired NOMA users. Lastly, we derive the analytical expressions of the ergodic rate for both of the paired NOMA users and calculate the asymptotic expressions for the typical user. The analytical results indicate that 1) the achievable rates reach an upper limit when the length of RIS increases; 2) exploiting RISs can enhance the path loss intercept to improve the performance without influencing the bandwidth. The simulation results show that 1) RIS-aided networks have superior performance than the networks without RISs; and 2) the SIC order in NOMA systems can be altered since RISs are able to change the channel quality of NOMA users.

A novel intelligent reconfigurable surface (IRS) assisted coordinated multi-point (CoMP) system is proposed. Our objective is to maximize the energy efficiency (EE) of this system by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment, power allocation, and optimal design of the IRS, while satisfying the users’ quality of service requirements. Considering the amplitude and phase shift characteristics, both ideal and non-ideal IRS are investigated. The formulated problem is proved to be NP-hard. By analyzing its structure, we decouple it into the power allocation sub-problem, the BS clustering, UE association, and sub-carrier assignment sub-problem, and the reflection coefficients design sub-problem. For the power allocation sub-problem, we invoke the fractional programming to find the optimal solution. For the reflection coefficients design sub-problem of ideal IRS, the optimal solution is derived with the Lagrangian dual method. Whereas quantization-based method is employed to find the discrete phase shifts for non-ideal IRS. We finally propose a genetic algorithm (GA) to represent the potential solutions of sub-carrier assignment, and combine the other two optimization algorithms as fitness estimator in GA. Numerical results validate the feasibility, fast convergence, and the flexibility of the proposed algorithm. It shows that the proposed scheme outperform the system without IRS and that with a random initialized IRS.

In this paper, we propose a deep state-action-reward-state-action (SARSA)$\lambda $learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency communication (URLLC). To reduce the mean decoding error probability in time-varying network environments, this work designs a reliable learning algorithm for providing a long-term resource allocation, where the reward feedback is based on the instantaneous network performance. With the aid of the proposed algorithm, this paper addresses three main challenges of the reliable resource sharing in NOMA-URLLC networks: 1) user clustering; 2) Instantaneous feedback system; and 3) Optimal resource allocation. All of these designs interact with the considered communication environment. Lastly, we compare the performance of the proposed algorithm with conventional Q-learning and SARSA Q-learning algorithms. The simulation outcomes show that: 1) Compared with the traditional Q learning algorithms, the proposed solution is able to converge within 200 episodes for providing as low as$10^{-2}$long-term mean error; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of decoding error probabilities; and 3) The proposed feedback system is efficient for the long-term learning process.

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).

The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is investigated, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. Based on the introduced basic signal model of `STAR', three practical operating protocols for STAR-RISs are proposed, namely energy splitting (ES), mode switching (MS), and time switching (TS). Moreover, a STAR-RIS aided downlink communication system is considered for both unicast and multicast transmission, where a multi-antenna base station (BS) sends information to two users, i.e., one on each side of the STAR-RIS. A power consumption minimization problem for the joint optimization of the active beamforming at the BS and the passive transmission and reflection beamforming at the STAR-RIS is formulated for each of the proposed operating protocols, subject to communication rate constraints of the users. For ES, the resulting highly-coupled non-convex optimization problem is solved by an iterative algorithm, which exploits the penalty method and successive convex approximation. Then, the proposed penalty-based iterative algorithm is extended to solve the mixed-integer non-convex optimization problem for MS. For TS, the optimization problem is decomposed into two subproblems, which can be consecutively solved using state-of-the-art algorithms and convex optimization techniques. Finally, our numerical results reveal that: 1) the TS and ES operating protocols are generally preferable for unicast and multicast transmission, respectively; and 2) the required power consumption for both scenarios is significantly reduced by employing the proposed STAR-RIS instead of conventional reflecting/transmiting-only RISs.

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a promising technology that aids in achieving full-space coverage on both sides of the surface, by splitting the incident signal into transmitted and reflected signals. This paper investigates the resource allocation problem in a STAR-RIS-assisted multi-carrier communication networks. To maximize the system sum-rate, a joint optimization problem comprising of the channel assignment, power allocation, and transmission and reflection beamforming at the STAR-RIS for orthogonal multiple access (OMA) is first formulated. To solve this challenging problem, we first propose a channel assignment scheme utilizing matching theory and then invoke the alternating optimization-based method to optimize the resource allocation policy and beamforming vectors iteratively. Furthermore, the sum-rate maximization problem for non-orthogonal multiple access (NOMA) with flexible decoding orders is investigated. To efficiently solve it, we first propose a location-based matching algorithm to determine the sub-channel assignment, where a transmitted user and a reflected user are grouped on a sub-channel. Based on thistransmission-and-reflectionsub-channel assignment strategy, a three-step approach is proposed, which involves the optimization of decoding orders, beamforming-coefficient vectors, and power allocation, by employing semidefinite programming, convex upper bound approximation, and geometry programming, respectively. Numerical results unveil that: 1) For OMA, a general design that includes the same-side user-pairing for channel assignment is preferable, whereas for NOMA, the proposed transmission-and-reflection scheme can achieve comparable performance to the exhaustive search-based algorithm. 2) The STAR-RIS-aided NOMA network significantly outperforms networks employing conventional RISs and OMA.

The reconfigurable intelligent surface (RIS) is one of the promising technologies contributing to the next generation smart radio environment. A novel physics-based RIS channel model is proposed. In the model, the signal reflected through the scatters and the RIS elements are jointly studied as multipath components of the overall received envelope. This novel strategy simplifies the mathematical structure of the channel gain and is able to compactly derive the distribution of the overall channel. For the case of continuous phase shifts, the distribution depends on the number of elements of the RIS and the observing direction of the receiver. For the case of discrete phase shifts, the distribution further depends on the number of phase quantization levels. The scaling law of the average received power is obtained from the scale factor of the distribution. For the application scenarios where RIS functions as an anomalous reflector, we investigate the performance of single RIS-assisted multiple access networks for time-division multiple access (TDMA), frequency-division multiple access (FDMA), and non-orthogonal multiple access (NOMA). Closed-form expressions for the outage probability of the proposed channel model are derived. It is proved that a constant diversity order exists, which is independent of the number of RIS elements. Simulation results are presented to confirm that the proposed model applies effectively to the element-based RISs.

A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collected Twitter data with geo-tags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting because of the superiority of LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geo-aware embedding approach for the events popularity forecasting. Furthermore, we associate the predict latent events and the popularity of the events with the caching strategy. Lastly, we propose a non-orthogonal multiple access (NOMA) based content transmission scheme. Extensive practical experiments demonstrate that: 1) the proposed TAC framework outperforms conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests; 2) the proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data; 3) the perplexity of the proposed skip-gram based LSTM is lower compared with conventional LDA approach; and 4) evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75% with smaller caching space compared to conventional algorithms. Simulation results also shows that the proposed NOMA-enabled caching scheme outperforms conventional least frequently used (LFU) scheme by 25%.

Intelligent reflecting surface (IRS) is a promising technology to enhance the coverage and performance of wireless networks. We consider the application of IRS to non-orthogonal multiple access (NOMA), where a base station transmits superposed signals to multiple users by the virtue of an IRS. The performance of an IRS-assisted NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) is investigated by invoking 1-bit coding scheme. In particular, we derive new exact and asymptotic expressions for both outage probability and ergodic rate of the$m$-th user with ipSIC/pSIC. Based on analytical results, the diversity order of the$m$-th user with pSIC is in connection with the number of reflecting elements and channel ordering. The high signal-to-noise radio (SNR) slope of ergodic rate for the$m$-th user is obtained. The throughput and energy efficiency of IRS-NOMA networks are discussed both in delay-limited and delay-tolerant transmission modes. Additionally, we derive new exact expressions of outage probability and ergodic rate for IRS-assisted orthogonal multiple access (IRS-OMA). Numerical results are presented to substantiate our analyses and demonstrate that: i) The outage behaviors of IRS-NOMA are superior to that of IRS-OMA and relaying schemes; ii) The$M$-th user has a larger ergodic rate than IRS-OMA and benchmarks. However, the ergodic performance of the$m$-th user exceeds relaying schemes in the low SNR regime; and iii) The IRS-assisted NOMA networks have ability to achieve the enhanced energy efficiency compared to conventional cooperative communications.

This paper investigates the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access system, where the legitimate users send confidential signals to the base station by exploiting STAR-RIS to reconfigure the electromagnetic propagation environment proactively. Depending on the availability of the eavesdropping channel state information (CSI), both the full CSI and statistical CSI of the eavesdropper are considered. For the full eavesdropping CSI scenario, we adopt the adaptive-rate wiretap code scheme with the aim of maximizing minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. To proceed, we propose an alternating hybrid beamforming (AHB) algorithm to jointly optimize the receive beamforming, transmit power, and reflection/transmission coefficients. While for the statistical eavesdropping CSI scenario, the constant-rate wiretap code scheme is employed to minimize the maximum secrecy outage probability (SOP) subject to the quality-of-service requirements of legitimate users. Then, we derive the exact SOP expression under the constant-rate coding strategy and develop an extended AHB algorithm for the joint secrecy beamforming design. Simulation results demonstrate the effectiveness of the proposed scheme. Moreover, some useful guidance about the quantification of phase shift/amplitude and the deployment of STAR-RIS is provided.

Grant-free (GF) transmission holds promise in terms of low latency communication by directly transmitting messages without waiting for any permissions. However, collision situations may frequently happen when limited spectrum is occupied by numerous GF users. The non-orthogonal multiple access (NOMA) technique can be a promising solution to achieve massive connectivity and fewer collisions for GF transmission by multiplexing users in power domain. We utilize a semi-grant-free (semi-GF) NOMA scheme for enhancing network connectivity and spectral efficiency by enabling grant-based (GB) and GF users to share the same spectrum resources. With the aid of semi-GF protocols, uplink NOMA networks are investigated by invoking stochastic geometry techniques. We propose a noveldynamic protocolto interpret which part of the GF users are allocated in NOMA transmissions via transmitting various channel quality thresholds by an added handshake. We utilize open-loop protocol with a fixed average threshold as the benchmark to investigate performance improvement. It is observed that dynamic protocol provides more accurate channel quality thresholds than open-loop protocol, thereby the interference from the GF users is reduced to a large extent. We analyze the outage performance and diversity gains under two protocols. Numerical results demonstrate that dynamic protocol is capable of enhancing the outage performance than open-loop protocol.

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

Compared with the conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting intelligent omini-surfaces (STAR-IOSs) are able to achieve 360° coverage “smart radio environments”. By splitting the energy or altering the active number of STAR-IOS elements, STAR-IOSs provide high flexibility of successive interference cancellation (SIC) orders for non-orthogonal multiple access (NOMA) systems. Based on the aforementioned advantages, this paper investigates a STAR-IOS-aided downlink NOMA network with randomly deployed users. We first propose three tractable channel models for different application scenarios, namely the central limit model, the curve fitting model, and the M-fold convolution model. More specifically, the central limit model fits the scenarios with large-size STAR-IOSs while the curve fitting model is extended to evaluate multi-cell networks. However, these two models cannot obtain accurate diversity orders. Hence, we figure out the M-fold convolution model to derive accurate diversity orders. We consider three protocols for STAR-IOSs, namely, the energy splitting (ES) protocol, the time switching (TS) protocol, and the mode switching (MS) protocol. Based on the ES protocol, we derive closed-form analytical expressions of outage probabilities for the paired NOMA users by the central limit model and the curve fitting model. Based on three STAR-IOS protocols, we derive the diversity gains of NOMA users by the M-fold convolution model. The analytical results reveal that the diversity gain of NOMA users is equal to the active number of STAR-IOS elements. Numerical results indicate that 1) in high signal-to-noise ratio regions, the central limit model performs as an upper bound of the simulation results, while a lower bound is obtained by the curve fitting model; 2) the TS protocol has the best performance but requesting more time blocks than other protocols; 3) the ES protocol outperforms the MS protocol as the ES protocol has higher diversity gains.

A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. Since ground mobile users are considered as roaming continuously, the UAVs need to be re-deployed timely based on the movement of users. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional deep Q-network (DQN) algorithm, the MDQN algorithm enables the experience of multi-agent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm is capable of converging under minor constraints and has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than invoking the circular trajectory and the 2D trajectory, respectively.

A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable of providing NOMA with artificial controlled channel conditions, which can be seen as a beneficial operation condition to obtain NOMA gains. To optimize the sum rate of all users, a deep deterministic policy gradient (DDPG) algorithm is invoked to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. In order to improve the efficiency and effectiveness of agent training for the DDPG agents, a federated learning (FL) concept is adopted to enable multiple agents to simultaneously explore similar environments and exchange experiences. We also proved that with the same random exploring policy, the FL armed deep reinforcement learning (DRL) agents can theoretically obtain a reward gain comparing to the independent agents. Our simulation results indicate that the mobile RIS scheme can significantly outperform the fixed RIS paradigm, which provides about three times data rate gain compared to the fixed RIS paradigm. Moreover, the NOMA scheme is capable of achieving a gain of 42% in contrast with the OMA scheme in terms of the sum rate. Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm has a superior convergence rate and optimization performance than the independent training framework.

A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate in the following: 1) with the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.

In this paper, we propose a deep state-action-reward-state-action (SARSA)$A$learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency communication (URLLC). To reduce the mean decoding error probability in time-varying network environments, this work designs a reliable learning algorithm for providing a long-term resource allocation, where the reward feedback is based on the instantaneous network performance. With the aid of the proposed algorithm, this paper addresses three main challenges of the reliable resource sharing in NOMA-URLLC networks: 1) Dynamic user clustering; 2) Instantaneous feedback system; and 3) Optimal resource allocation. All of these designs interact with the considered communication environment. The simulation outcomes show that: 1) Compared with the traditional Q learning algorithm, the proposed solution converges faster and obtains better performance; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of decoding error probabilities; and 3) The dynamic feedback system is efficient for the long-term learning process.

This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.

This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a distributed IRS enabled NOMA transmission framework is proposed to serve users securely in the presence of a passive eavesdropper. Considering that the instantaneous channel state information (CSI) of the eavesdropper is challenging to acquire in practice, we utilize the secrecy outage probability (SOP) as the security metric. A problem by jointly optimizing the transmit power at the base station (BS) and reflection phase shifts at IRSs, subject to the successive interference cancellation (SIC) decoding constraints and SOP constraints, is formulated to maximize the minimum secrecy rate among legitimate users. To tackle the non-convex problem, we first derive the exact SOP in closed-form expressions and then propose a novel ring-penalty based successive convex approximation (SCA) algorithm to design power allocation and phase shifts jointly. Numerical results validate the convergence and the secrecy superiority of proposed scheme over the baseline schemes.

To achieve 360ºcoverage, we investigate a simulta-neous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) aided downlink non-orthogonal multiple access (NOMA) network with randomly deployed users. For different scenarios, we first derive two STAR- RIS-aided channel models, namely the central limit model and the curve fitting model. More specifically, the central limit model fits the scenarios with numerous RIS elements while the curve fitting model can be extended to multi-cell scenarios. The analytical results reveal that 1) the central limit model has closed-form expressions calculated as the error functions, and 2) the curve fitting model can be closely modeled as a Gamma distribution. We then derive the closed-form outage probability expressions for the NOMA users. Numerical results indicate that 1) the two channel models match the simulation results well in low signal-to-noise-ratio (SNR) regions and perform as boundaries in high SNR regions, 2) the central limit model performs as an upper bound of the simulation results, while a lower bound can be obtained by the curve fitting model, and 3) the both users in the NOMA pair have no error floor.

A novel reconfigurable intelligent surfaces (RISs)-based transmission framework is proposed for downlink non-orthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and the power allocation at the base station (BS). A model-agnostic meta-learning (MAML)-based learning algorithm is proposed to solve the joint optimization problem with a fast convergence rate and low model complexity. Extensive simulation results demonstrate that the proposed QoS-based NOMA network achieves significantly higher transmission throughput compared to the conventional orthogonal multiple access (OMA) network. It can also be observed that substantial throughput gain can be achieved by integrating RISs in NOMA and OMA networks. Moreover, simulation results of the proposed QoS-based clustering method demonstrate observable throughput gain against the conventional channel condition-based schemes.

In this paper, a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) system is proposed, where the STAR-RIS can simultaneously transmit and reflect the incident signals. Our objective is to maximize the achievable sum rate by jointly optimizing the decoding order, power allocation coefficients, active beamforming, transmission and reflection beamformings. However, the formulated problem is non-convex with intricately coupled variables. To tackle this challenge, a suboptimal two-layer iterative algorithm is proposed. Specifically, in the inner-layer iteration, for a given decoding order, the power allocation coefficients, active beamforming, transmission and reflection beamformings are optimized alternatively. For the outer-layer iteration, the decoding order of NOMA users in each cluster is updated with the solutions obtained from the inner-layer iteration. Simulation results are provided to demonstrate that the proposed STAR-RSI-NOMA system outperforms conventional RIS assisted systems.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging and the effectiveness of such a solution is limited due to the absence of closed-loop power control. In this paper, we design a prototype of layer-based transmit power pool by utilizing multi-agent reinforcement learning to provide open-loop power control and offload the computing tasks at the base station (BS) side. IoT users in each layer decide their own transmit power level from this layer-based power pool, instead of transmitting on the allocated sub-channel with allocated transmit power level. The proposed algorithm does not require any information exchange between IoT users and does not rely on any assistance from the BS. Numerical results confirm that the double deep Q network based GF-NOMA algorithm achieves high accuracy and finds out an accurate transmit power level for each layer. Moreover, the proposed GF-NOMA system outperforms the traditional GF with orthogonal multiple access techniques in terms of throughput.

A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of all robots by jointly optimizing trajectories and NOMA decoding orders of robots, reflecting coefficients of the IRS, and the power allocation of the AP, subject to the quality of service (QoS) of each robot. To tackle this problem, a dueling double deep Q-network (D3QN) based algorithm is invoked for jointly determining the phase shift matrix and robots’ trajectories. Specifically, the trajectories for robots contain a set of local optimal positions, which reveals that robots make the optimal decision at each step. Numerical results demonstrated that the proposed D3QN algorithm outperforms the conventional algorithm, while the performance of IRS-NOMA network is better than the orthogonal multiple access (OMA) network.

This paper investigates intelligent reflecting surface (IRS)-assisted systems, where an access point sends independent information to multiple users with the aid of one IRS. Our goal is to characterize the capacity region of the IRS-assisted multiuser communication systems. We jointly optimize the discrete phase-shift matrix of the IRS and resource allocation with the capacity-achieving non-orthogonal multiple access (NOMA) transmission scheme. The Pareto boundary of the capacity region is characterized by maximizing the average sum rate of all users, subject to a set of rate-profile constraints, total transmit power and discrete IRS phase shift constraints. Though the formulated problem is non-convex, we derive the globally optimal solutions by invoking the Lagrange duality method. It is shown that the optimal transmission strategy is alternating transmission among different user groups by dynamically adjusting the IRS phase shifts. We further propose a Hadamard codebook based scheme, which serves as a lower bound on the optimal performance gains. Numerical results demonstrate that: i) the IRS is capable of significantly improving the capacity region; ii) the capacity region achieved by the Hadamard codebook based scheme is close to that of discrete phase shifts for a small number of IRS elements.

The reconfigurable intelligent surface (RIS) is one of the promising technology contributing to the next generation smart radio environment. The application scenarios include massive connectivity support, signal enhancement, and security protection. One crucial difficulty of analyzing the RIS-assisted networks is that the channel performance is sensitive to the change of user receiving direction. This paper tackles the problem by categorizing the RIS illuminated space into four categories: perfect alignment, coherent alignment, random alignment, and destructive alignment. These four categories cover all the possible phase alignment conditions that a user could experience within the overall 2 pi solid angle of RIS-illuminated space. We perform analysis for different categories, deriving analytical expressions for the outage probability and diversity order. Simulation results are presented to confirm the effectiveness of the proposed analytical results.

Reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems, i.e., enhancing the channel quality and altering the SIC orders. Invoked by stochastic geometry methods, we investigate the downlink coverage performance of RIS-aided multi-cell NOMA networks. We first derive the RIS-aided channel model, concluding the direct and reflecting links. The analytical results demonstrate that the RIS-aided channel model can be closely modeled as a Gamma distribution. Additionally, interference from other cells is analyzed. Lastly, we derive closed-form coverage probability expressions for the paired NOMA users. Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks. In the grant-free NOMA systems with a massive number of users, user activity detection is of great importance. Existing multi-user detection (MUD) techniques rely on complicated update steps which may cause latency in signal detection. In this paper, we propose a generative neural network-based MUD (GenMUD) framework to utilize low-complexity neural networks, which are trained to reconstruct signals in a small fixed number of steps. By exploiting the uncorrelated user behaviours, we design a network architecture to achieve higher recovery accuracy with a low computational cost. Experimental results show significant performance gains in detection accuracy compared to conventional solutions under different channel conditions and user sparsity levels. We also provide a sparsity estimator through extensive experiments. Simulation results of the sparsity estimator showed high estimation accuracy, strong robustness to channel variations and neglectable impact on support detection accuracy.

The recent advances in low earth orbit (LEO) satellites enable the satellites to provide task processing capability for remote Internet-of-Things (IoT) mobile devices (IMDs) without proximal multiaccess edge computing (MEC) servers. In this article, by leveraging the LEO satellites, a novel MEC framework for terrestrial-satellite IoT is proposed. With the aid of terrestrial-satellite terminal (TST), the computation offloading from IMDs to LEO satellites is divided into two stages in the ground and space segments. In order to minimize the weighted-sum energy consumption of IMDs, we decompose the formulated problem into two layered subproblems: 1) the lower layer subproblem minimizing the latency of space segment, which is solved by sequential fractional programming with attaining the first-order optimality and 2) the upper layer subproblem that is solved by exploiting the convex structure and applying the Lagrangian dual decomposition method. Based on the solutions to the two layered subproblems, an energy-efficient computation offloading and resource allocation algorithm (E-CORA) is proposed. By simulations, it is shown that: 1) there exists a specific amount of offloading bits, which can minimize the energy consumption of IMDs and the proposed E-CORA outperforms full offloading and local computing only; 2) larger transmit power of the TST helps to save the energy of IMDs; and 3) by increasing the number of visible satellites, the ratio of offloading bits increases while the energy consumption of IMDs can be decreased.

A novel framework is proposed for integrating reconfigurable intelligent surfaces (RIS) in unmanned aerial vehicle (UAV) enabled wireless networks, where an RIS is deployed for enhancing the service quality of the UAV. Non-orthogonal multiple access (NOMA) technique is invoked to further improve the spectrum efficiency of the network, while mobile users (MUs) are considered as roaming continuously. The energy consumption minimizing problem is formulated by jointly designing the movement of the UAV, phase shifts of the RIS, power allocation policy from the UAV to MUs, as well as determining the dynamic decoding order. A decaying deep Q-network (D-DQN) based algorithm is proposed for tackling this pertinent problem. In the proposed D-DQN based algorithm, the central controller is selected as an agent for periodically observing the state of UAV-enabled wireless network and for carrying out actions to adapt to the dynamic environment. In contrast to the conventional DQN algorithm, the decaying learning rate is leveraged in the proposed D-DQN based algorithm for attaining a tradeoff between accelerating training speed and converging to the local optimal. Numerical results demonstrate that: 1) In contrast to the conventional Q-learning algorithm, which cannot converge when being adopted for solving the formulated problem, the proposed D-DQN based algorithm is capable of converging with minor constraints; 2) The energy dissipation of the UAV can be significantly reduced by integrating RISs in UAV-enabled wireless networks; 3) By designing the dynamic decoding order and power allocation policy, the RIS-NOMA case consumes 11.7% less energy than the RIS-OMA case.

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology. The problem of joint deployment, phase shift design, as well as power allocation in the multiple-input-single-output (MISO) NOMA network is formulated for maximizing the energy efficiency with considering users particular data requirements. To tackle this pertinent problem, machine learning approaches are adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. Secondly, a decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS. In the proposed algorithm, the base station, which controls the RIS by a controller, acts as an agent. The agent periodically observes the state of the RIS-enhanced system for attaining the optimal deployment and design policies of the RIS by learning from its mistakes and the feedback of users. Additionally, it is proved that the proposed D3QN based deployment and design algorithm is capable of converging within mild conditions. Simulation results are provided for illustrating that the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between the prediction accuracy and computational complexity. Finally, it is demonstrated that the proposed D3QN based algorithm outperforms the benchmarks, while the NOMA-enhanced RIS system is capable of achieving higher energy efficiency than orthogonal multiple access (OMA) enabled RIS system.

Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternately solved by exploiting the penalty-based method and the successive convex approximation technique. The proposed BCD-based algorithm is demonstrated to be able to obtain a stationary point of the original problem with polynomial time complexity. Numerical results show that: 1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum rate compared to the benchmark schemes, i.e., orthogonal multiple access (OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing performance gain for multi-UAV networks by both enhancing channel qualities of UAVs to their served users and mitigating the inter-UAV interference; and 3) optimizing the UAV placement can make the sum rate gain brought by NOMA more distinct due to the flexible decoding order design.

This paper considers a two-user downlink transmission in intelligent reflecting surface (IRS) aided network over fading channels. Particularly, non-orthogonal multiple access (NOMA) and two orthogonal multiple access (OMA) schemes, namely, time division multiple access (TDMA) and frequency division multiple access (FDMA), are studied. The objective is to maximize the system average sum rate for the delay-tolerant transmission. We propose two adjustment schemes, namely, dynamic phase adjustment and one-time phase adjustment. The power budget, minimum average data rate, and discrete unit modulus reflection coefficient are considered as constrains. To solve the problem, two phase shifters adjustment algorithms with low complexity are proposed to obtain near optimal solutions. With given phase shifters and satisfaction of time-sharing condition, the optimal resource allocations are obtained using the Lagrangian dual decomposition. The numerical results reveal that: i) the average sum rate of proposed NOMA network aided by IRS outperforms the conventional NOMA network over fading channels; ii) with continuous IRS adjustment in the fading block, the proposed TDMA scheme performs better than the FDMA scheme; iii) increasing the minimum average user rate requirement has less impact on the proposed IRS-NOMA system than on the IRS-OMA system.

The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this article. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized by jointly optimizing the IRS reflection matrix and wireless resource allocation under the constraints of a maximum number of IRS reconfiguration times. In NOMA, all users are served in the same resource blocks by employing superposition coding and successive interference cancelation techniques. In OMA, all users are served by being allocated orthogonal resource blocks of different sizes. For NOMA, the ideal case with an asymptotically large number of IRS reconfiguration times is firstly considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.

Multi-access mobile edge computing (MEC) allows each user to offload partial task bits to multiple MEC servers via multi-access radio transmission, enabling the collaboration of edge computing. By leveraging the advantage of nonorthogonal multiple access (NOMA) in improving the transmission efficiency, it is expected to effectively reduce the energy consumption of users in multi-access MEC with the aid of NOMA. However, considering the co-channel interference of NOMA and computation collaboration among MEC servers, the joint task offloading and resource allocation is a challenging problem. In this article, with the objective to minimize the weighted sum energy of users, we first propose optimal task offloading and resource allocation algorithms for the special single-user (OTORA-SU) and general multi-user (OTORA-MU) cases, by exploiting the convex and layered structure of the formulated problem. Interestingly, it is found that the single user always preferentially offloads task bits to the MEC servers with better channel gains, regardless of the computation capacity of each MEC server. Then, a low-complexity algorithm (LTORA-MU) is proposed for the general multi-user case, which converges fast and achieves near-optimal performances. Considering the channel estimation error, the impacts of imperfect channel state information (CSI) and successive interference cancellation (SIC) are also investigated. Simulation results demonstrate that for both perfect and imperfect CSI and SIC, 1) the proposed OTORA-SU and LTORA-MU outperform the local computing, full offloading, FDMA-based offloading and non-collaborative MEC schemes; 2) as the number of MEC servers grows, the ratio of offloading task bits increases while the energy consumption is decreased.

Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This article investigates the security problems for dual UAV-assisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs' trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves a joint optimization of NOMA user partitioning and RIS phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. To solve the challenging joint optimization problem, we invoke a modified object migration automation (MOMA) algorithm to partition the users into equal-size clusters. To optimize the RIS phase shifting matrix, we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Different from conventional training-then-testing processing, we consider a long-term self-adjusting learning model where the intelligent agent is capable of learning the optimal action for every given state through exploration and exploitation. Extensive numerical results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves enhanced sum data rate compared with the conventional orthogonal multiple access (OMA) framework. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy in a long-term manner. 3) The performance of the proposed RIS-aided NOMA framework can be improved by increasing the granularity of the RIS phase shifts. The numerical results also show that increasing the number of reflecting elements (REs) is an efficient method to improve the sum data rate of the MUs.

This paper considers the downlink of reconfigurable intelligent surface (RIS) assisted cooperative non-orthogonal multiple access (CNOMA) systems. Our objective is to minimize the total transmit power by jointly optimizing the active beamforming vectors, transmit-relaying power, and RIS phase shifts. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem. To tackle this problem, the alternating optimization approach is utilized to decouple the variables. In each alternative procedure, the optimal solutions for the active beamforming vectors, transmit-relaying power and phase shifts are obtained. However, the proposed algorithm has high complexity since the optimal phase shifts are solved by integer linear programming (ILP) whose computational complexity is exponential in the number of variables. To strike a good computational complexity-optimality trade-off, a low-complexity suboptimal algorithm is proposed by adopting the iterative penalty function based semidefinite programming (SDP) and the successive refinement approaches. Numerical results illustrate that: i) the proposed RIS-CNOMA system, aided by our proposed algorithms, outperforms the conventional CNOMA system. ii) the proposed low-complexity suboptimal algorithm can achieve near-optimal performance. iii) whether the RIS-CNOMA system outperforms the RIS assisted non-orthogonal multiple access (RIS-NOMA) system depends not only on the users’ locations but also on the RIS’s location.

Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput.

In this paper, non-orthogonal multiple access (NOMA) networks assisted by multiple intelligent reflecting surfaces (IRSs) with discrete phase shifts are investigated, in which each user device (UD) is served by an IRS to improve the quality of the received signal. Two scenarios are considered according to whether there is a direct link between the base station (BS) and each UD, and the outage performance is analyzed for each of them. Specifically, the asymptotic expressions for the upper and lower bounds of the outage probability in the high signal-to-noise ratio (SNR) regime are derived. Following that, the diversity order is obtained. It is shown that the use of discrete phase shifts does not degrade diversity order. More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance. Simulation results also imply the superiority of IRSs over full-duplex decode-and-forward relays.

Intelligent reflecting surfaces (IRSs) are envisioned to provide reconfigurable wireless environments for future communication networks. In this paper, both downlink and uplink IRS-aided non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) networks are studied, in which an IRS is deployed to enhance the coverage by assisting a cell-edge user device (UD) to communicate with the base station (BS). To characterize system performance, new channel statistics of the BS-IRS-UD link with Nakagami-$m$fading are investigated. For each scenario, the closed-form expressions for the outage probability and ergodic rate are derived. To gain further insight, the diversity order and high signal-to-noise ratio (SNR) slope for each scenario are obtained according to asymptotic approximations in the high-SNR regime. It is demonstrated that the diversity order is affected by the number of IRS reflecting elements and Nakagami fading parameters, but the high-SNR slope is not related to these parameters. Simulation results validate our analysis and reveal the superiority of the IRS over the full-duplex decode-and-forward relay.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due to the absence of closed-loop power control. We design a prototype of transmit power pool (PP) to provide open-loop power control. IoT users acquire their transmit power in advance from this prototype PP solely according to their communication distances. Firstly, a multi-agent deep Q-network (DQN) aided GF-NOMA algorithm is proposed to determine the optimal transmit power levels for the prototype PP. More specifically, each IoT user acts as an agent and learns a policy by interacting with the wireless environment that guides them to select optimal actions. Secondly, to prevent the Q-learning model overestimation problem, double DQN (DDQN) based GF-NOMA algorithm is proposed. Numerical results confirm that the DDQN based algorithm finds out the optimal transmit power levels that form the PP. Comparing with the conventional online learning approach, the proposed algorithm with the prototype PP converges faster under changing environments due to limiting the action space based on previous learning. The considered GF-NOMA system outperforms the networks with fixed transmission power, namely all the users have the same transmit power and the traditional GF with orthogonal multiple access techniques, in terms of throughput.

Integrating robots into cellular networks creating connected robotic users has emerged as a promising technology for future smart cities and smart factories due to their low cost and high maneuverability. However, the requirement of establishing stable and high-quality communication links to the robotic users greatly restricts their applicability, especially in indoor environments where obstacles may block the wireless link. To tackle this challenge, in this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user scenarios are considered. In the single-user scenario, one mobile robotic user (MRU) communicates with the AP. In the multiple-user scenario, the AP serves one MRU and one static robotic user (SRU) employing either non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) transmission. The considered system is optimized for minimization of the travelling time/distance of the MRU from a given starting point to a predefined final location, while satisfying constraints on the communication quality of the robotic users. To this end, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. For the single-user scenario, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the MRU for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory. For the multiple-user scenario, a communication rate map is constructed, which characterizes the spatial distribution of the maximum expected rate of the MRU for the optimal power allocation at the AP and the optimal IRS phase shifts subject to a minimum rate requirement for the SRU. The joint optimization problem is efficiently solved by invoking bisection search and successive convex approximation methods. Then, a graph theory based solution for the robot path planning problem is derived by exploiting the obtained communication rate map. Our numerical results show that: 1) the required travelling distance of the MRU can be significantly reduced by deploying an IRS; 2) NOMA yields a higher communication rate for the MRU than OMA; 3) the IRS performance gain is significantly more pronounced for NOMA than for OMA.

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging thetime-selectivenature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where alocal region optimizationmethod is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2)asymmetricandsymmetricIRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.

With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV’s mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV’s trajectory, based on the Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

Being able to accommodate multiple simultaneous transmissions on a single channel, non-orthogonal multiple access (NOMA) appears as an attractive solution to support massive machine type communication (mMTC) that faces a massive number of devices competing to access the limited number of shared radio resources. In this paper, we first analytically study the throughput performance of NOMA-based random access (RA), namely NOMA-RA. We show that while increasing the number of power levels in NOMA-RA leads to a further gain in maximum throughput, the growth of throughput gain is slower than linear. This is due to the higher-power dominance characteristic in power-domain NOMA known in the literature. We explicitly quantify the throughput gain for the very first time in this paper. With our analytical model, we verify the performance advantage of NOMA-RA scheme by comparing with the baseline multi-channel slotted ALOHA (MS-ALOHA), with and without capture effect. Despite the higher-power dominance effect, the maximum throughput of NOMA-RA with four power levels achieves over three times that of the MS-ALOHA. However, our analytical results also reveal the sensitivity of load on the throughput of NOMA-RA. To cope with the potential bursty traffic in mMTC scenarios, we propose adaptive load regulation through a practical user barring algorithm. By estimating the current load based on the observable channel feedback, the algorithm adaptively controls user access to maintain the optimal loading of channels to achieve maximum throughput. When the proposed user barring algorithm is applied, simulations demonstrate that the instantaneous throughput of NOMA-RA always remains close to the maximum throughput confirming the effectiveness of our load regulation.

In this paper, a downlink non-orthogonal multiple access (NOMA) system is studied, in which an intelligent reflecting surface (IRS) is deployed to enhance the coverage by assisting a cell-edge user device (UD) to communicate with the base station (BS). To characterize system performance, new channel statistics for the BS-IRS-UD link with Nakagami-m fading are investigated. Based on the new channel statistics, the closed-form expression for the outage probability is derived. To gain further insight into the problem, the diversity order is obtained according to the asymptotic approximation in the high signal-to-noise ratio (SNR) regime. It is demonstrated that the diversity order is affected by the number of IRS elements and Nakagami fading parameters. Simulation results validate our analysis and reveal the superiority of the IRS over the full-duplex decode-and-forward relay in the high-SNR regime.

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order and power allocation coefficient vector, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the performance of IRS-NOMA system is better than IRS-OMA system.

This paper considers a two-user downlink intelligent reflecting surface (IRS) assisted network over fading channels. Particularly, non-orthogonal multiple access (NOMA) and two orthogonal multiple access (OMA) schemes, namely, time division multiple access (TDMA) and frequency division multiple access (FDMA), are studied. Our goal is maximizing the system average sum rate for delay-tolerant transmission. The power budget, minimum average user rate and discrete unit modulus reflection coefficient constraints are considered as constraints. To solve the problem, phase shifters adjustment algorithms with low complexity are proposed to obtain high quality solutions. With given phase shifters, the optimal power allocation is obtained using the Lagrangian dual decomposition. Numerical results demonstrate the performance gain by introducing IRS into the system as well as the utility of the proposed algorithms.

The cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA) are investigated in this paper. The delivery of multi-media contents for the mixed augmented reality (AR) and normal multi-media application is assisted by multiple mobile UAV base stations, which cache popular contents for wireless backhaul link traffic offloading. To cope with the dynamic content requests and mobility of users in practical scenarios, the dynamic optimization problem for user association, caching placement of UAVs, real-time deployment of UAVs, and power allocation of NOMA is modeled as a stackelberg game to minimize the long-term content delivery delay. Specifically, the game is decomposed into a leader level problem and a number of follower level problems. A correction mechanism is added in deep reinforcement learning (DRL) to optimize the user association in leader level. A meta actor network is proposed in DRL to jointly optimize the UAVs caching placement, real-time UAVs deployment and power allocation of NOMA in follower level. Then, a dynamic caching placement and resource allocation algorithm based on multi-agent meta deep reinforcement learning is proposed to minimize the long-term content delivery delay. Finally, we demonstrate that the considerable gains are achieved by the proposed algorithm.

The reconfigurable intelligent surface (RIS) is one of the promising technology contributing to the next generation smart radio environment. A novel RIS-assisted wireless channel model is proposed. Particularly, we consider the RIS and the scattering environment as a whole by studying the signal's multipath propagation. The model suggests that the RIS-assisted wireless channel can be treated as Ricianly distributed. Analytical expressions are derived for the shape factor and the scale factor of the distribution, which depend on detailed RIS parameters, such as phase-alignment accuracy and the number of elements. Closed-form expressions for the outage probability of the proposed channel model are derived. It is proved that a constant diversity order exists, which is independent of the number of RIS elements. Simulation results are presented to confirm that the proposed model applies effectively to the phased-array implemented RISs.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves the optimization of phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. For intelligently adjusting the phase shifting matrix of the access point (AP), we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Extensive simulation results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves better sum data rate compared with orthogonal multiple access (OMA) networks. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy, while conventional optimization approaches can not. 3) Compared with increasing the transmit power of the AP, increasing the number of reflecting elements (REs) is a more efficiency method to improve the sum data rate.

By activating blocked users and altering successive interference cancellation (SIC) sequences, reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems. This work investigates the downlink performance of RIS-aided NOMA networks via stochastic geometry. We first introduce the unique path loss model for RIS reflecting channels. Then, we evaluate the angle distributions based on a Poisson cluster process (PCP) framework, which theoretically demonstrates that the angles of incidence and reflection are uniformly distributed. Lastly, we derive closed-form expressions for coverage probabilities of the paired NOMA users. Our results show that 1) RIS-aided NOMA networks perform better than the traditional NOMA networks; and 2) the SIC order in NOMA systems can be altered since RISs are able to change the channel gains of NOMA users.

This paper conceive a large intelligent surface (LIS)aided multiple-input multiple-output network for providing wireless services to randomly roaming users. The network performance is analyzed by utilizing stochastic geometry tools. We aim for serving multiple users by jointly designing the passive beamforming weight at LISs and detection weight vectors at users. As a benefit, the interference imposed by the LISs can be suppressed. In an effort to evaluate the performance of the proposed network, we first derive approximated channel statistics for characterizing the effective channel gains. Then, we derive closed-form expressions for the ergodic rate of users. For gleaning further insights, we investigate the high-signal-to-noise-ratio (SNR) of ergodic rate. Our analytical results demonstrate that the specific fading environments encountered between the LISs and users have almost no impact on the ergodic rate attained.

This paper investigates a massive non-orthogonal multiple access (NOMA) enhanced Internet of Things (IoT) network. In order to provide massive connectivity, a novel cluster strategy is proposed, where massive devices can be served simultaneously. New channel statistics are derived. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA enhanced IoT networks is capable of outperforming orthogonal multiple access (OMA) enhanced IoT networks.

A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. To solve this pertinent problem, a double deep Q-network (DDQN) algorithm is proposed for making collision-free decisions. Thus, the trajectory and velocity of the AV are determined by receiving real-time traffic information from the base stations (BSs). Compared to the conventional deep Q-network algorithm, the proposed DDQN algorithm is capable of overcoming the large overestimation of action values by decomposing the max-Q-value operation into action selection and action evaluation. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

In this article, we investigate the content distribution in the hotspots area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and caching placement are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment and caching placement for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into two sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement by greedy algorithm. Finally, we propose a low complexity iteration algorithm for the joint UAV deployment and caching placement optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that the proposed algorithm obtains the near-optimal solution of exhaustive search, converges within several iterations and achieves better performance compared with the benchmark algorithms.

Reconfigurable intelligent surface (RIS) is a promising solution to improve spectrum efficiency and promote cost- effectively wireless communication in the future. In this paper, RIS is deployed between a single-antenna base station (BS) and multiple single-antenna users to assist downlink non-orthogonal multiple access (NOMA) transmission. Considering the fairness among users, our goal is jointly optimizing the power allocation, decoding order, and the phase shifts to maximize the minimum user rate under total power constraint. To solve this minimum rate maximization problem, the optimal power allocation and the optimal fair rate are first revealed with a given phase shift vector. Then, the phase shift vector is optimized via maximizing the worst channel gain, which can determine the lower bound of the fair rate. The phase shift vector optimization problem is relaxed to a convex semidefinite program (SDP) and an efficient algorithm is proposed to obtain a rank-one solution. Simulation results show that our proposed algorithm can enhance the fair rate compared to the conventional scheme.