A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications

TL;DR

This paper proposes a DRL-based method to optimize reflection profiles in RIS-assisted multi-receiver systems, significantly improving beam gain and width while reducing optimization time.

Contribution

It introduces a novel DRL-based optimization approach for dual-reflection scenarios, addressing profile misalignment issues in RIS-assisted communications.

Findings

Achieves 1.2 dB reflection peak gain improvement

Outperforms random and exhaustive search methods

Reduces optimization time significantly

Abstract

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.