In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, the pointing accuracy and intensity of reflections depend crucially on the 'profile,' representing the amplitude/phase state information of all elements in a RIS array. The superposition of multiple single-reflection profiles enables multi-reflection for distributed users. However, the optimization challenges from periodic element arrangements in single-reflection and multi-reflection profiles are understudied. The combination of periodical single-reflection profiles leads to amplitude/phase counteractions, affecting the performance of each reflection beam. This paper focuses on a dual-reflection optimization scenario and investigates the far-field performance deterioration caused by the misalignment of overlapped profiles. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based optimization method. Comparative experiments against random and exhaustive searches demonstrate that our proposed DRL method outperforms both alternatives, achieving the shortest optimization time. Remarkably, our approach achieves a 1.2 dB gain in the reflection peak gain and a broader beam without any hardware modifications.

本研究通过引入一种新颖的深度强化学习（DRL）优化方法，针对双反射场景中重叠剖面不一致导致的远场性能退化问题展开研究。与随机和穷举搜索方法进行对比实验，结果表明我们提出的DRL方法不仅在优化时间上表现出色，而且获得了1.2 dB的反射峰值增益和更宽的波束，而无需进行任何硬件修改。

一种基于DRL的用于RIS辅助多接收机通信的反射增强方法