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In reconfigurable intelligent surface (RIS)-assisted wireless communicationsystems, the pointing accuracy and intensity of reflections depend crucially onthe 'profile,' representing the amplitude/phase state information of allelements in a RIS array. The superposition of multiple single-reflectionprofiles enables multi-reflection for distributed users. However, theoptimization challenges from periodic element arrangements in single-reflectionand multi-reflection profiles are understudied. The combination of periodicalsingle-reflection profiles leads to amplitude/phase counteractions, affectingthe performance of each reflection beam. This paper focuses on adual-reflection optimization scenario and investigates the far-fieldperformance deterioration caused by the misalignment of overlapped profiles. Toaddress this issue, we introduce a novel deep reinforcement learning(DRL)-based optimization method. Comparative experiments against random andexhaustive searches demonstrate that our proposed DRL method outperforms bothalternatives, achieving the shortest optimization time. Remarkably, ourapproach achieves a 1.2 dB gain in the reflection peak gain and a broader beamwithout any hardware modifications.

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