Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays

TL;DR

This paper introduces a MARL-based control framework for RIS reflector arrays that bypasses CSI estimation, enabling adaptive, high-performance wireless communication in dynamic environments.

Contribution

It develops a novel multi-agent reinforcement learning approach with a reduced-order spatial model for practical, CSI-free RIS control in complex radio environments.

Findings

Achieves up to 26.86 dB signal enhancement over static reflectors.

Demonstrates rapid adaptation to user mobility in NLOS scenarios.

Maintains stable coverage despite localization noise.

Abstract

Reconfigurable Intelligent Surfaces (RIS) are pivotal for next-generation smart radio environments, yet their practical deployment is severely bottlenecked by the intractable computational overhead of Channel State Information (CSI) estimation. To bypass this fundamental physical-layer barrier, we propose an AI-native, data-driven paradigm that replaces complex channel modeling with spatial intelligence. This paper presents a fully autonomous Multi-Agent Reinforcement Learning (MARL) framework to control mechanically adjustable metallic reflector arrays. By mapping high-dimensional mechanical constraints to a reduced-order virtual focal point space, we deploy a Centralized Training with Decentralized Execution (CTDE) architecture. Using Multi-Agent Proximal Policy Optimization (MAPPO), our decentralized agents learn cooperative beam-focusing strategies relying on user coordinates, achieving CSI-free operation. High-fidelity ray-tracing simulations in dynamic non-line-of-sight (NLOS) environments demonstrate that this multi-agent approach rapidly adapts to user mobility, yielding up to a 26.86 dB enhancement over static flat reflectors and outperforming single-agent and hardware-constrained DRL baselines in both spatial selectivity and temporal stability. Crucially, the learned policies exhibit good deployment resilience, sustaining stable signal coverage even under 1.0-meter localization noise. These results validate the efficacy of MARL-driven spatial abstractions as a scalable, highly practical pathway toward AI-empowered wireless networks.