Joint Transmit and Pinching Beamforming for PASS: Optimization-Based or Learning-Based?

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 novel pinching beamforming design. 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-Tranformer 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 30% system performance than MM-PDD algorithm, while achieving a millisecond-level response on modern GPUs.

Electromagnetic Channel Statistics for Continuous-Aperture Array (CAPA) Systems

The channel statistics of a continuous-aperture array (CAPA)-based channel are analyzed using its continuous electromagnetic (EM) properties. The received signal-to-noise ratio (SNR) is discussed under isotropic scattering conditions. Using Landau's theorem, the eigenvalues of the autocorrelation of the EM fading channel are shown to exhibit a step-like behavior. Building on this, closed-form expressions for the probability distribution of the SNR and the average channel capacity are derived. Numerical results are provided to validate the accuracy of the derivations.

Downlink and Uplink ISAC in Continuous-Aperture Array (CAPA) Systems

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 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 (SIC)-based methods are proposed to manage inter-functionality interference. Using the subspace approach along with the time-sharing technique, closed-form expressions for the optimal beamformers are derived, and the achievable CRs, SRs, and rate region are analyzed. 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 (SPDA)-based ISAC.

Beam Training for Pinching-Antenna Systems (PASS)

This article investigates the beam training design problems for pinching-antenna systems (PASS), 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 scheme can significantly reduce the training overhead compared to the two-dimensional exhaustive search, while maintaining reasonable rate performance; ii) compared to fixed-location pinching antennas and conventional array antennas, the proposed dynamic pinching antennas yield better flexibility and improved performance.

GPASS: Deep Learning for Beamforming in Pinching-Antenna Systems (PASS)

A novel GPASS architecture is proposed for jointly learning pinching beamforming and transmit beamforming in pinching antenna systems (PASS). The GPASS is with a staged architecture, where the positions of pinching antennas are first learned by a sub-GNN. Then, the transmit beamforming is learned by another sub-GNN based on the antenna positions. The sub-GNNs are incorporated with the permutation property of the beamforming policy, which helps improve the learning performance. The optimal solution structure of transmit beamforming is also leveraged to simplify the mappings to be learned. Numerical results demonstrate that the proposed architecture can achieve a higher SE than a heuristic baseline method with low inference complexity.

Diffusion Model for Multiple Antenna Communications

The potential of applying diffusion models (DMs) for multiple antenna communications is discussed. A unified framework of applying DM for multiple antenna tasks is first proposed. Then, the tasks are innovatively divided into two categories, i.e., decision-making tasks and generation tasks, depending on whether an optimization of system parameters is involved. For each category, it is conceived 1) how the framework can be used for each task and 2) why the DM is superior to traditional artificial intelligence (TAI) and conventional optimization tasks. It is highlighted that the DMs are well-suited for scenarios with strong interference and noise, excelling in modeling complex data distribution and exploring better actions. A case study of learning beamforming with a DM is then provided, to demonstrate the superiority of the DMs with simulation results. Finally, the applications of DM for emerging multiple antenna technologies and promising research directions are discussed.

Pinching Antenna Systems (PASS): Architecture Designs, Opportunities, and Outlook

This article proposes a novel design for the Pinching Antenna Systems (PASS) and advocates simple yet efficient wireless communications over the `last meter'. First, the potential benefits of PASS are discussed by reviewing an existing prototype. Then, the fundamentals of PASS are introduced, including physical principles, signal models, and communication designs. In contrast to existing multi-antenna systems, PASS brings a novel concept termed \emph{Pinching Beamforming}, which is achieved by dynamically adjusting the positions of PAs. Based on this concept, a couple of practical transmission architectures are proposed for employing PASS, namely non-multiplexing and multiplexing architectures. More particularly, 1) The non-multiplexing architecture is featured by simple baseband signal processing and relies only on the pinching beamforming; while 2) the multiplexing architecture provides enhanced signal manipulation capabilities with joint baseband and pinching beamforming, which is further divided into sub-connected, fully-connected, and phase-shifter-based fully-connected schemes. Furthermore, several emerging scenarios are put forward for integrating PASS into future wireless networks. As a further advance, by demonstrating a few numerical case studies, the significant performance gain of PASS is revealed compared to conventional multi-antenna systems. Finally, several research opportunities and open problems of PASS are highlighted.

STAR-RIS-Enabled Multi-Path Beam Routing with Passive Beam Splitting

Reconfigurable intelligent surfaces (RISs) can be densely deployed in the environment to create multi-reflection line-of-sight (LoS) links for signal coverage enhancement. However, conventional reflection-only RISs can only achieve half-space reflection, which limits the LoS path diversity. In contrast, simultaneously transmitting and reflecting RISs (STAR-RISs) can achieve full-space reflection and transmission, thereby creating more LoS paths. Hence, in this paper, we study a new multi-STAR-RIS-aided communication system, where a multi-antenna base station (BS) transmits to multiple single-antenna users by exploiting the signal beam routing over a set of cascaded LoS paths each formed by multiple STAR-RISs. To reveal essential insights, we first consider a simplified single-user case, aiming to maximize its received signal power by jointly optimizing the active beamforming at the BS, the BS's power allocation over different paths, the number of selected beam-routing paths, the selected STAR-RISs for each path, as well as their amplitude and phase shifts for transmission/reflection. However, this problem is difficult to be optimally solved as different paths may be intricately coupled at their shared STAR-RISs. To tackle this difficulty, we first derive the optimal solution to this problem in closed-form for a given set of paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Next, we extend the proposed clique-based method to the multi-user case to maximize the minimum received signal power among all users, subject to additional constraints on the disjointness of the selected paths for different users. Simulation results show that our proposed STAR-RIS-enabled beam routing outperforms the conventional beam routing with reflection-only RISs in both single- and multi-user cases.

Design and Prototyping of Filtering Active STAR-RIS with Adjustable Power Splitting

Reconfigurable Intelligent Surfaces (RISs) have emerged as a transformative technology for next-generation wireless communication systems, offering unprecedented control over electromagnetic wave propagation. In particular, Simultaneously Transmitting and Reflecting RISs (STAR-RISs) have garnered significant attention due to their full-space coverage. This paper presents an active STAR-RIS, which enables independent control of both transmission and reflection phases and features out-of-band harmonic suppression. Unlike the traditional passive RIS, the proposed design integrates active amplification to overcome the inherent passive losses, significantly enhancing signal strength and system performance. Additionally, the system supports dynamic power allocation between transmission and reflection modes, providing greater flexibility to meet diverse communication demands in complex propagation environments. The versatility of the design is further validated by extending the Radar Cross Section (RCS)-based path loss model to the STAR-RIS. This design improves efficiency, flexibility, and adaptability, offering a promising solution for future wireless communication systems, particularly in scenarios requiring simultaneous control of transmission and reflection signals.

On the Sum Rate and User Fairness of STAR-RIS Aided Communications

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated. A robust joint beamforming design problem under the imperfect channel state information (CSI) is formulated to maximize the weighted sum of the Jain's fairness index and the normalized system sum rate. To solve this non-convex problem, an alternating optimization (AO) algorithm is proposed, which leverages the S-Procedure, successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results demonstrate that with proposed algorithm: 1) various trade-offs between sum rate and user fairness can be achieved; 2) a larger trade-off region can be achieved by adopting STAR-RIS compared to conventional RIS; and 3) the performance degradation caused by imperfect CSI is less than 7% with our proposed robust beamforming approach.