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-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.

翻译：本文提出了一种支持夹持天线系统（PASS）的新型下行多用户多输入单输出（MISO）框架。PASS由多个跨越数千个波长的波导组成，这些波导配备了大量低成本介质粒子，称为夹持天线（PAs），用于将信号辐射到自由空间。PAs的位置可重新配置以改变信号的大尺度路径损耗和相位，从而促进新型夹持波束成形设计。本文构建了一个和速率最大化问题，通过联合优化发射与夹持波束成形，自适应地实现建设性信号增强与破坏性干扰抑制。为解决这一高度耦合且非凸的问题，提出了基于优化和基于学习的两种方法。1) 对于基于优化的方法，开发了一种最大化-最小化与惩罚对偶分解（MM-PDD）算法，该算法使用Lipschitz代理函数处理非凸复指数分量，然后调用PDD进行问题解耦。2) 对于基于学习的方法，提出了一种新颖的Karush-Kuhn-Tucker（KKT）引导的对偶学习（KDL）方法，该方法通过学习对偶变量以数据驱动的方式重构KKT解。基于此思想，开发了KDL-Transformer算法，该算法通过注意力机制捕获PA间/用户间依赖关系以及信道状态信息（CSI）-波束成形依赖关系。仿真结果表明：i) 所提出的PASS框架即使仅使用少量PAs，其性能也显著优于传统大规模多输入多输出（MIMO）系统。ii) 所提出的KDL-Transformer相比MM-PDD算法可提升超过20%的系统性能，同时在现代GPU上实现毫秒级响应。