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.

翻译：在可重构智能表面（RIS）辅助的无线通信系统中，反射的指向精度和强度关键取决于“配置文件”，即RIS阵列中所有单元的幅度/相位状态信息。多个单反射配置文件的叠加可实现面向分布式用户的多反射。然而，由单反射和多反射配置文件中周期性单元排布所引入的优化挑战尚未得到充分研究。周期性单反射配置文件的组合会导致幅度/相位相互抵消，从而影响各反射波束的性能。本文聚焦于双反射优化场景，研究因重叠配置文件失配导致的远场性能恶化问题。为解决该问题，我们提出了一种基于深度强化学习（DRL）的新型优化方法。与随机搜索和穷举搜索的对比实验表明，我们提出的DRL方法在优化耗时方面优于这两种方案，实现了最快的优化速度。值得注意的是，该方法在不修改硬件的前提下，实现了1.2 dB的反射峰值增益提升以及更宽的波束宽度。