发表机构
School of Information Science and Engineering, Shandong University; State Key Laboratory of Mathematical Sciences, AMSS, Chinese Academy of Sciences; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen; School of Mathematical Sciences, Beijing University of Posts and Telecommunications（山东大学信息科学与工程学院; 中国科学院数学与系统科学研究院数学科学学院国家重点实验室; 香港中文大学（深圳）理工学院; 北京邮电大学数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多用户夹持天线系统中的端口选择与预编码联合优化问题，提出一种无需标签的离散扩散方法，通过势函数引导采样，在0.30%性能差距内实现超150倍加速。
AI 中文摘要
夹持天线系统（PASS）通过沿介电波导的选定位置激活辐射元件来重构无线信道。端口选择是提高多用户PASS系统性能的核心问题。本文研究了多用户和速率最大化中的联合端口选择与预编码器设计问题，该问题导致一个大规模非凸混合整数问题，将离散端口选择与连续预编码器设计耦合在一起。我们提出了一种离散扩散方法，该方法不需要最优或接近最优的端口选择解决方案作为训练标签，并将由此产生的优化问题重新表述为从目标分布中采样。相反，我们从相邻可行实现之间的局部目标差异中学习一个势函数。将学习到的势函数纳入Metropolis-Hastings采样规则，以指导并行扩散过程，同时保持端口选择约束。仿真结果表明，所提出的方法在0.30%的和速率差距内接近穷举搜索，同时实现了超过150倍的加速，并且优于包括贪婪搜索和束搜索在内的最先进方法。特别是，它相对于贪婪搜索还实现了18.8倍的加速。
英文摘要
Pinching-antenna systems (PASS) reconfigure wireless channels by activating radiating elements at selected locations along dielectric waveguides. Port selection is a core issue in improving the performance of multiuser PASS systems. In this paper, we study joint port selection and precoder design for multiuser sum rate maximization, which leads to a large-scale nonconvex mixed-integer problem coupling discrete port selection with continuous precoder design. We propose a discrete diffusion method that does not require optimal or near-optimal port selection solutions as training labels and recasts the resulting optimization problem as sampling from a target distribution. Instead, we learn a potential function from local objective differences between neighboring feasible realizations. The learned potential is incorporated into a Metropolis--Hastings sampling rule to guide a parallel diffusion process while preserving the port selection constraints. Simulation results show that the proposed method approaches exhaustive search within a $0.30\%$ sum rate gap while achieving over $150\times$ speedup, and outperforms state-of-the-art methods including greedy and beam search. In particular, it also achieves an $18.8\times$ speedup over greedy search.
