This article analyzes the achievable sum-rate of multiuser uplink segmented waveguide-enabled pinching-antenna systems (SWANs). To unveil system-design insights, an upper bound on the achievable sum-rate is derived, based on which the existence of an optimal segment activation level is theoretically established. Motivated by this result, hybrid segment selection and aggregation (HSS/A) schemes are proposed to jointly optimize segment activation and pinching-antenna (PA) placement. Correspondingly, low-complexity greedy algorithms are developed for the considered optimization problem. Numerical results validate the theoretical analysis and demonstrate that the proposed HSS/A schemes outperform conventional full-segment aggregation.

A low-overhead site-specific multi-user multiple-input multiple-output (MU-MIMO) beamforming framework is proposed. Conventional limited-feedback MU-MIMO relies on channel state information reference signal (CSI-RS) transmission and user feedback before grouping and beamforming, which requires substantial online overhead when the antenna dimension and candidate-user pool are large. To reduce this burden, the proposed framework exploits site-specific information (SSI), which captures local radio propagation features. By learning the mapping from low-overhead beam-domain observations to effective transmit spatial subspaces of users, the BS can infer inter-user separability before high-resolution CSI acquisition and construct a compact group-level CSI acquisition subspace for the selected users. This site-specific design can be implemented within the standard limited-feedback procedure using synchronization signal block (SSB)-based reference signal received power (RSRP) fingerprints for subspace inference and CSI-RS feedback for low-dimensional CSI refinement. Extensive numerical results demonstrate that the proposed framework can identify compatible user groups before CSI-RS acquisition, preserve most scheduled-user channel energy in a compact group subspace, and achieve higher effective rates than conventional systems with significantly lower overhead and user-side processing burden.

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.

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.

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.

This paper studies a pinching-antenna system (PASS)-enabled integrated sensing and communication (ISAC) framework, where pinching beamforming is realized by adaptively adjusting the pinching-antenna (PA) positions along waveguides to simultaneously multicast common information and perform target localization. Specifically, a maxmin fairness (MMF) criterion and the Bayesian Cramer-Rao bound (BCRB) are formulated to quantify the multicasting and sensing performance, respectively. Based on these metrics, a sensing-centric joint design framework is developed to minimize the BCRB subject to multicast quality-of-service (QoS) constraints. A sequential element-wise alternating optimization (AO) algorithm is proposed to efficiently optimize the PA positions for pinching beamforming. Numerical results demonstrate that: (i) the proposed PASSenabled ISAC framework achieves substantially improved sensing accuracy compared with conventional multiple-input multipleoutput (MIMO) schemes while guaranteeing multicast service requirements; and (ii) the localization performance is consistently enhanced as the number of deployed PAs increases.

Pinching-antenna systems (PASS)-enabled communications are a promising paradigm. However, obtaining accurate channel state information (CSI) is challenging due to the limited pilots, model mismatch, and other hardware impairments. In this letter, we study the robust beamforming design for PASS-based multi-user communication under the CSI uncertainty. In particular, we model the CSI error using a complex-ball uncertainty set and study the worst-case beamforming design to minimize the transmit power for satisfying users' minimum achievable rate requirements. To address this problem, the S-procedure is first applied to convert the resulting semi-infinite formulation into a tractable finite set of constraints. Then, an alternating optimization framework is developed to design the baseband beamforming and pinching beamforming. The baseband beamforming subproblem is handled via the semidefinite programming, while a penalty dual decomposition-based method is developed to address the non-convex pinching beamforming subproblem. Finally, the effectiveness of the proposed algorithm is verified through simulations.

Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation in wireless networks. However, constructing and maintaining a high-fidelity WDT over large-scale complex environments can be prohibitively expensive, especially in terms of data acquisition, geometric reconstruction, storage, and ray tracing. To address this issue, a task-oriented nonuniform refinement framework for WDTs is proposed, where limited resources are selectively allocated to the WDT components that matter most to wireless fidelity. Specifically, a unified refinement framework is first developed, which maximizes task-level fidelity under resource constraints through fine-grained component-wise fidelity allocation. This framework is then instantiated for building-level geometry refinement in urban WDTs. It is found that different buildings exhibit highly heterogeneous impacts on wireless fidelity. Motivated by this observation, an ellipsoid-guided selective refinement algorithm (EGSR) is proposed. By jointly considering the relevance of each building to both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation paths, its refinement priority can be estimated using only a low-fidelity WDT. Simulations across multiple urban scenarios show that EGSR can substantially improve radio-map fidelity and preserve beamforming effectiveness by refining only a small subset of buildings. These results demonstrate the potential of task-oriented fidelity allocation as a scalable principle for constructing efficient and performance-aware WDTs, thereby facilitating reliable site-specific learning and optimization.

This study investigates the application of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided medium-Earth-orbit 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 and successive interference cancellation 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. By doing so, it introduces a new challenge: how to select the optimal satellite to simultaneously maximize data rate and ensure high-accuracy positioning. To meet above-mentioned diverse application needs, navigation-oriented INAC and communication-oriented INAC have been developed, each tailored according to distinct power allocation factors. We then proposed two algorithms, namely navigation-prioritized-algorithm and communication-prioritized-algorithm, to improve the navigation or communication performance by selecting the satellite with the optimized position dilution of precision or with the best channel gain. The effectiveness of the proposed STAR-RIS-aided INAC network is quantified by analyzing the positioning error for navigation services and by evaluating communication performance through achievable ergodic rate metrics. Our satellite selection approach indicates that: 1) The positioning services at the urban canyon users can be completed with the aid of STAR-RIS. 2) Additionally, it is observed that while a single STAR-RIS array can extend the navigational link, it fails to serve users in indoor scenarios, highlighting a limitation in the current system design.

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 uncrewed 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.

To facilitate intelligent beam training, a large language model (LLM)-empowered beam training framework is proposed for the pinching antenna system (PASS) in downlink multi-user multiple-input multiple-output (MIMO) communications. A novel LLM-based beam training supervised learning mechanism is developed, wherein multimodal information is encoded into high-dimensional tokens to guide the adaptive probing process. For the multi-user case, a joint codebook generation and beam selection problem is formulated based on the system sum rate under the minimum mean square error (MMSE) transmit beamforming. The training labels for pinching beamforming are constructed by selecting the beam combination that maximizes system performance from each user’s Top-S candidate beams. Based on pretrained Generative Pre-trained Transformers (GPTs), the LLM is trained in an end-to-end fashion to minimize the cross-entropy loss. Simulation results demonstrate that the proposed LLM-enabled PASS framework significantly outperforms both the LLM-based massive MIMO and conventional PASS beam training, achieving up to 57.14% and 33.33% improvements in sum rate, respectively.

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.

A joint communication and control (JCC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users (CUs) and controls a physical plant in a closed loop. In the downlink, BS-generated control inputs are transmitted to and recovered at the plant, with wireless actuation distortion incorporated into the plant-state evolution. In the uplink, the plant state is reported to the BS and tracked by a Kalman filter (KF) for subsequent control-input generation. To characterize long-term control performance under communication-control interference, finite- and infinite-horizon linear quadratic Gaussian (LQG) costs are derived, directly linking beamforming design to plant-state evolution. JCC beamforming problems are then formulated for vector- and scalar-valued control inputs to minimize the infinite-horizon LQG cost subject to per-user communication signal-to-interference-plus-noise ratio (SINR) requirements. For the vector case, a second-order cone programming (SOCP)-based successive convex approximation method is developed for the resulting nonconvex problem. For the scalar case, a closed-form infinite-horizon LQG cost is derived, and the communication-control Pareto boundary is optimally characterized by an SOCP-based bisection method. Its optimality follows from the strict monotonicity of the scalar control cost with respect to the control SINR. Numerical results show that the derived costs closely match Monte Carlo simulations, the KF accurately tracks the ground-truth plant-state trajectory, and the proposed methods consistently outperform the zero-forcing benchmark. This confirms the benefit of balancing communication-control interference, especially with limited spatial degrees of freedom (DoFs).

A framework of continuous-aperture array (CAPA)-based integrated sensing and communications (ISAC) under a fading communication channel is proposed. A continuous operator-based signal model is developed, and the statistics of the communication channel gain are characterized via Landau's eigenvalue theorem. On this basis, the performance of the CAPA-based ISAC system is analyzed by considering three continuous beamforming designs: i) the sensing-centric (S-C) design that optimizes sensing performance, ii) the communication-centric (C-C) design that optimizes communication performance, and iii) the Pareto-optimal design that balances the sensing-communication trade-off. For the S-C and C-C design, closed-form expressions for the sensing rate (SR), ergodic communication rate (CR), and outage probability are derived, and high-signal-to-noise ratio asymptotic analysis is conducted to obtain the multiplexing and diversity gains. For the Pareto-optimal design, the Pareto-optimal beamformer achieving the Pareto boundary is derived, and the achievable SR-CR region is characterized. Numerical results demonstrate that the proposed CAPA-ISAC scheme outperforms both conventional spatially discrete arrays-based ISAC and CAPA-based frequency-division sensing and communications.

The widespread use of unmanned aerial vehicles (UAVs) in low-altitude airspace has raised significant safety and security concerns, motivating the development of reliable non-cooperative UAV surveillance technologies. Integrated sensing and communication (ISAC), enabled by multiple-input multiple-output (MIMO) architectures and orthogonal frequency-division multiplexing (OFDM) waveforms, has emerged as a promising paradigm for leveraging cellular infrastructure to support large-scale sensing without additional hardware deployment. This paper presents the first comprehensive survey dedicated to MIMO OFDM-enabled ISAC for low-altitude non-cooperative UAV surveillance, where the targeted UAVs do not intentionally assist the monitoring system through dedicated signaling or prior coordinate sharing. We first analyze the unique propagation characteristics of low-altitude UAV sensing, including severe clutter, rapid channel variations, and mixed near/far-field effects, and discuss corresponding waveform design principles. We then systematically review existing MIMO OFDM-enabled UAV surveillance techniques along four key dimensions: ISAC system modeling and network optimization, UAV detection and tracking algorithms under single and networked base station (BS) architectures, UAV identification techniques based on micro-Doppler and learning-based approaches, and experimental validations and practical field trials. Subsequently, we summarize open challenges such as sensing under severe clutter and multipath, data scarcity for identification, cooperative multi-BS fusion, and real-world deployment constraints. Finally, we outline promising future research directions toward 5G-Advanced (5G-A) and 6G-enabled low-altitude surveillance systems.

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 is an element of-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 is an element of -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 multiple waveguide 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 stretched PAs on waveguides and collects the echo sensing signals with the mounted ULA. Based on this configuration, a target sensing Cramer Rao Bound (CRB) minimization problem is formulated under communication quality-of-service (QoS) constraints, power budget constraints, and PA deployment constraints. An alternating optimization (AO) method is employed to address the formulated non-convex optimization problem. Simulation results demonstrate that the proposed PASS assisted ISAC framework achieves superior performance over benchmark schemes.