标题： 联合发射和夹紧波束成形用于夹紧天线系统（PASS）：基于优化的还是基于学习的？ 显示英文标题

标题： Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based?

摘要： 一种基于新型夹紧天线系统（PASS）的下行链路多用户多输入单输出（MISO）框架被提出。 PASS由跨越数千个波长的多个波导组成，配备大量低成本的介质粒子，称为夹紧天线（PAs），用于将信号辐射到自由空间。 PAs的位置可以重新配置，以改变信号的大尺度路径损耗和相位，从而促进新型夹紧波束成形设计。 制定了一个总速率最大化问题，该问题联合优化发射和夹紧波束成形，以自适应地实现建设性信号增强和破坏性干扰抑制。 为了解决这个高度耦合且非凸的问题，提出了基于优化的方法和基于学习的方法。 1）对于基于优化的方法，开发了一种极大极小和惩罚对偶分解（MM-PDD）算法，该算法使用Lipschitz替代函数处理非凸复指数成分，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建KKT解。 按照这一思路，开发了一种KDL-Transformer算法，该算法通过注意力机制捕捉PA之间/用户之间以及信道状态信息（CSI）-波束成形之间的依赖关系。 仿真结果表明：i）所提出的PASS框架即使使用少量PAs也显著优于传统的大规模多输入多输出（MIMO）系统。 ii）所提出的KDL-Transformer在现代GPU上实现毫秒级响应的同时，系统性能比MM-PDD算法提高超过30%。 显示英文摘要

摘要： 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.