Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays
Summary: arXiv:2604.05162v1 Announce Type: new
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.
Introduction
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. This approach allows for efficient training and execution of multiple agents that can collaboratively manage the reflector arrays.
Key Features of the Proposed System
- Multi-Agent Proximal Policy Optimization (MAPPO): The decentralized agents learn cooperative beam-focusing strategies relying on user coordinates, achieving CSI-free operation.
- High-Fidelity Ray-Tracing Simulations: Simulations in dynamic non-line-of-sight (NLOS) environments demonstrate that this multi-agent approach rapidly adapts to user mobility.
- Performance Enhancement: The proposed framework yields up to a 26.86 dB enhancement over static flat reflectors.
- Comparison with Baselines: It outperforms single-agent and hardware-constrained Deep Reinforcement Learning (DRL) baselines in both spatial selectivity and temporal stability.
- Deployment Resilience: The learned policies exhibit good deployment resilience, sustaining stable signal coverage even under 1.0-meter localization noise.
Results and Implications
The results validate the efficacy of MARL-driven spatial abstractions as a scalable, highly practical pathway toward AI-empowered wireless networks. By eliminating the reliance on complex CSI estimation, this approach significantly reduces computational overhead while enhancing the adaptability and performance of wireless communication systems.
Conclusion
In summary, this innovative framework not only addresses the CSI bottleneck but also presents a forward-thinking solution for the deployment of Reconfigurable Intelligent Surfaces in smart radio environments. The use of Multi-Agent Reinforcement Learning opens new avenues for efficient spatial control, paving the way for more resilient and adaptive wireless networks.