A DRL-based Reflection Enhancement Method for RIS-assisted
  Multi-receiver Communications
Wei Wang Peizheng Li Angela Doufexi Mark A Beach
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.