Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays

TL;DR

This paper introduces a multi-agent reinforcement learning system that dynamically controls reflector arrays to significantly improve wireless signal reception in challenging environments, outperforming traditional methods.

Contribution

It proposes a novel MARL-based control scheme for reflector arrays that avoids complex channel estimation and adapts rapidly to user mobility in NLOS scenarios.

Findings

Substantial performance gains over single-agent RL baselines

Rapid adaptation to user movement within one simulation step

Validated scalability and robustness across various scenarios

Abstract

This paper presents a multi-agent reinforcement learning (MARL) approach for controlling adjustable metallic reflector arrays to enhance wireless signal reception in non-line-of-sight (NLOS) scenarios. Unlike conventional reconfigurable intelligent surfaces (RIS) that require complex channel estimation, our system employs a centralized training with decentralized execution (CTDE) paradigm where individual agents corresponding to reflector segments autonomously optimize reflector element orientation in three-dimensional space using spatial intelligence based on user location information. Through extensive ray-tracing simulations with dynamic user mobility, the proposed multi-agent beam-focusing framework demonstrates substantial performance improvements over single-agent reinforcement learning baselines, while maintaining rapid adaptation to user movement within one simulation step. Comprehensive evaluation across varying user densities and reflector configurations validates system scalability and robustness. The results demonstrate the potential of learning-based approaches for adaptive wireless propagation control.