Two-Stage Heterogeneous Graph Neural Network for RIS-Aided Physical-Layer Security

This paper investigates physical-layer security (PLS) enabled by graph neural networks (GNNs). We propose a two-stage heterogeneous GNN (HGNN) to maximize the secrecy energy efficiency (SEE) of a reconfigurable intelligent surface (RIS)-assisted multi-input-single-output (MISO) system that serves multiple legitimate users (LUs) and eavesdroppers (Eves). The first stage formulates the system as a bipartite graph involving three types of nodes-RIS reflecting elements, LUs, and Eves-with the goal of generating the RIS phase shift matrix. The second stage models the system as a fully connected graph with two types of nodes (LUs and Eves), aiming to produce beamforming and artificial noise (AN) vectors. Both stages adopt an HGNN integrated with a multi-head attention mechanism, and the second stage incorporates two output methods: beam-direct and model-based approaches. The two-stage HGNN is trained in an unsupervised manner and designed to scale with the number of RIS reflecting elements, LUs, and Eves. Numerical results demonstrate that the proposed two-stage HGNN outperforms state-of-the-art GNNs in RIS-aided PLS scenarios. Compared with convex optimization algorithms, it reduces the average running time by three orders of magnitude with a performance loss of less than $4\%$. Additionally, the scalability of the two-stage HGNN is validated through extensive simulations.

Multi-Mode Pinching-Antenna Systems: Mode Selection or Mode Combining?

This letter investigates multi-mode pinching antenna systems (PASS), where signals of multiple orthogonal modes can be transmitted within a dielectric waveguide and radiated by pinching antennas (PAs). This enables mode-domain multiplexing for efficient multi-user communications using a single waveguide. In particular, two operating protocols are proposed, namely mode selection and mode combining. Mode selection enforces each PA to predominantly radiate signal power of one single mode, while mode combining allows each PA to flexibly radiate power of multiple modes. Based on the two protocols, a sum rate maximization problem is formulated for multi-mode PASS-enabled multi-user downlink communications, where the transmit beamforming, PA positions, and PA propagation constants are jointly optimized. To address this rapidly oscillating and highly nonconvex problem, a particle swarm optimization (PSO) based Karush-Kuhn-Tucker (KKT)-parameterized beamforming (PSO- KPBF) algorithm is proposed. KKT-conditioned solutions are exploited to guide the swarm search, thus reducing the search space and achieving fast convergence. Numerical results demonstrate that: 1) Even using a simple uniform mode-combining design, the multi-mode PASS significantly outperform conventional single-mode PASS and hybrid beamforming systems; and 2) Mode combining achieves high spectral efficiency, while mode selection approximates its performance with a lower hardware complexity. Code is released at https://github.com/xiaoxiaxusummer/multi_mode_pinching_antenna

Exploiting Segmented Waveguide-Enabled Pinching-Antenna Systems (SWANs) for Uplink Tri-Hybrid Beamforming

A segmented waveguide-enabled pinching-antenna system (SWAN)-based tri-hybrid beamforming architecture is proposed for uplink multi-user MIMO communications, which jointly optimizes digital, analog, and pinching beamforming. Both fully-connected (FC) and partially-connected (PC) structures between RF chains and segment feed points are considered. For the FC architecture, tri-hybrid beamforming is optimized using the weighted minimum mean-square error (WMMSE) and zero-forcing (ZF) approaches. Specifically, the digital, analog, and pinching beamforming components are optimized via a closed-form solution, Riemannian manifold optimization, and a Gauss-Seidel search, respectively. For the PC architecture, an interleaved topology tailored to the SWAN receiver is proposed, in which segments assigned to each RF chain (sub-array) are interleaved with those from other sub-arrays. Based on this structure, a WMMSE-based tri-hybrid design is developed, in which the Riemannian-manifold update used for the FC structure is replaced by element-wise phase calibration to exploit sparsity in analog beamforming. To gain insight into the performance of the proposed system, the rate-scaling laws with respect to the number of segments are derived for both the FC and PC structures. Our results demonstrate that: i)~SWAN with the proposed tri-hybrid beamforming consistently outperforms conventional hybrid beamforming and conventional pinching-antenna systems with pinching beamforming for both the FC and PC structures; and ii)~the PC structure can strike a good balance between sum rate and energy consumption when the number of segments is large; and iii) the achievable rate does not necessarily increase with the number of segments.

When Movable Antennas Meet RSMA and RIS: Robust Beamforming Design With Channel Uncertainty

In this work, we propose an intelligent optimization framework for a multi-user communication system integrating movable antennas (MAs) and a reconfigurable intelligent surface (RIS) under the rate-splitting multiple access (RSMA) protocol. The system sum-rate is maximized through joint optimization of transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MA positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. Imperfect channel state information (CSI) is considered for all links, where robustness is ensured by modeling channel estimation errors within a bounded uncertainty region, guaranteeing worst-case performance reliability. The resulting non-convex problem is solved using an alternating optimization framework. The precoding subproblem is reformulated as a semidefinite programming (SDP) problem via linear matrix inequalities derived using the S-procedure. The RIS reflection matrix is optimized using successive convex approximation (SCA), yielding an equivalent SDP formulation. The MA position optimization is addressed through SCA combined with block coordinate descent (BCD) method. Numerical results validate the effectiveness of the proposed framework and demonstrate fast convergence.

Sampling-Free Diffusion Transformers for Low-Complexity MIMO Channel Estimation

Diffusion model-based channel estimators have shown impressive performance but suffer from high computational complexity because they rely on iterative reverse sampling. This paper proposes a sampling-free diffusion transformer (DiT) for low-complexity MIMO channel estimation, termed SF-DiT-CE. Exploiting angular-domain sparsity of MIMO channels, we train a lightweight DiT to directly predict the clean channels from their perturbed observations and noise levels. At inference, the least square (LS) estimate and estimation noise condition the DiT to recover the channel in a single forward pass, eliminating iterative sampling. Numerical results demonstrate that our method achieves superior estimation accuracy and robustness with significantly lower complexity than state-of-the-art baselines.

Multi-Mode Pinching Antenna Systems Enabled Multi-User Communications

This paper proposes a novel multi-mode pinching-antenna systems (PASS) framework. Multiple data streams can be transmitted within a single waveguide through multiple guided modes, thus facilitating efficient multi-user communications through the mode-domain multiplexing. A physic model is derived, which reveals the mode-selective power radiation feature of pinching antennas (PAs). A two-mode PASS enabled two-user downlink communication system is investigated. Considering the mode selectivity of PA power radiation, a practical PA grouping scheme is proposed, where each PA group matches with one specific guided mode and mainly radiates its signal sequentially. Depending on whether the guided mode leaks power to unmatched PAs or not, the proposed PA grouping scheme operates in either the non-leakage or weak-leakage regime. Based on this, the baseband beamforming and PA locations are jointly optimized for sum rate maximization, subject to each user's minimum rate requirement. 1) A simple two-PA case in non-leakage regime is first considered. To solve the formulated problem, a channel orthogonality based solution is proposed. The channel orthogonality is ensured by large-scale and wavelength-scale equality constraints on PA locations. Thus, the optimal beamforming reduces to maximum-ratio transmission (MRT). Moreover, the optimal PA locations are obtained via a Newton-based one-dimension search algorithm that enforces two-scale PA-location constraints by Newton's method. 2) A general multi-PA case in both non-leakage and weak-leakage regimes is further considered. A low-complexity particle-swarm optimization with zero-forcing beamforming (PSO-ZF) algorithm is developed, thus effectively tackling the high-oscillatory and strong-coupled problem. Simulation results demonstrate the superiority of the proposed multi-mode PASS over conventional single-mode PASS and fixed-antenna structures.

A Survey of Pinching-Antenna Systems (PASS)

The pinching-antenna system (PASS), recently proposed as a flexible-antenna technology, has been regarded as a promising solution for several challenges in next-generation wireless networks. It provides large-scale antenna reconfiguration, establishes stable line-of-sight links, mitigates signal blockage, and exploits near-field advantages through its distinctive architecture. This article aims to present a comprehensive overview of the state of the art in PASS. The fundamental principles of PASS are first discussed, including its hardware architecture, circuit and physical models, and signal models. Several emerging PASS designs, such as segmented PASS (S-PASS), center-fed PASS (C-PASS), and multi-mode PASS (M-PASS), are subsequently introduced, and their design features are discussed. In addition, the properties and promising applications of PASS for wireless sensing are reviewed. On this basis, recent progress in the performance analysis of PASS for both communications and sensing is surveyed, and the performance gains achieved by PASS are highlighted. Existing research contributions in optimization and machine learning are also summarized, with the practical challenges of beamforming and resource allocation being identified in relation to the unique transmission structure and propagation characteristics of PASS. Finally, several variants of PASS are presented, and key implementation challenges that remain open for future study are discussed.

User Localization and Channel Estimation for Pinching-Antenna Systems (PASS)

This letter proposes a novel user localization and channel estimation framework for pinching-antenna systems (PASS), where pinching antennas are grouped into subarrays on each waveguide to cooperatively estimate user/scatterer locations, thus reconstructing channels. Both single-waveguide (SW) and multi-waveguide (MW) structures are considered. SW consists of multiple alternatingly activated subarrays, while MW deploys one subarray on each waveguide to enable concurrent subarray measurements. For the 2D scenarios with a fixed user/scatter height, an orthogonal matching pursuit-based geometry-consistent localization (OMP-GCL) algorithm is proposed, which leverages inter-subarray geometric relationships and compressed sensing for precise estimation. Theoretical analysis on Cramér-Rao lower bound (CRLB) demonstrates that: 1) The estimation accuracy can be improved by increasing the geometric diversity through multi-subarray deployment; and 2) SW provides a limited geometric diversity within a $180^\circ$ half space and leads to angle ambiguity, while MW enables full-space observations and reduces overheads. The OMP-GCL algorithm is further extended to 3D scenarios, where user and scatter heights are also estimated. Numerical results validate the theoretical analysis, and verify that MW achieves centimeter- and decimeter-level localization accuracy in 2D and 3D scenarios with only three waveguides.

Segmented Waveguide-Enabled Pinching-Antenna Systems (SWANs) for ISAC

A segmented waveguide-enabled pinching-antenna system (SWAN)-assisted integrated sensing and communications (ISAC) framework is proposed. Unlike conventional pinching antenna systems (PASS), which use a single long waveguide, SWAN divides the waveguide into multiple short segments, each with a dedicated feed point. Thanks to the segmented structure, SWAN enhances sensing performance by significantly simplifying the reception model and reducing the in-waveguide propagation loss. To balance performance and complexity, three segment controlling protocols are proposed for the transceivers, namely i) \emph{segment selection} to select a single segment for signal transceiving, ii) \emph{segment aggregation} to aggregate signals from all segments using a single RF chain, and iii) \emph{segment multiplexing} to jointly process the signals from all segments using individual RF chains. The theoretical sensing performance limit is first analyzed for different protocols, unveiling how the sensing performance gain of SWAN scales with the number of segments. Based on this performance limit, the Pareto fronts of sensing and communication performance are characterized for the simple one-user one-target case, which is then extended to the general multi-user single-target case based on time-division multiple access (TDMA). Numerical results are presented to verify the correctness of the derivations and the effectiveness of the proposed algorithms, which jointly confirm the advantages of SWAN-assisted ISAC.

GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising

Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing. Secondly, we design a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN) to learn the distribution of high-resolution CSI, facilitating CSI generation and data augmentation to further improve HMGAT's performance. Finally, we present a Denoising Score Network (DSN) framework and its instantiation, termed DeBERT, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF. Experiments on DeepMIMO urban datasets demonstrate the proposed models' superior generalization, scalability, and robustness across various HBF tasks with perfect and imperfect CSI.