Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors

TL;DR

This paper introduces a CSI-free hierarchical multi-agent reinforcement learning framework for controlling reconfigurable reflectors in mmWave networks, significantly reducing computational overhead and improving signal strength.

Contribution

It presents a novel two-tier neural architecture that replaces CSI estimation with user localization data for scalable, cost-effective beam management in wireless environments.

Findings

Achieves up to 7.79 dB RSSI improvement over centralized methods.

Demonstrates robustness to localization errors and multi-user scalability.

Eliminates CSI overhead while maintaining high-fidelity beam focusing.

Abstract

Reconfigurable Intelligent Surfaces (RIS) has a potential to engineer smart radio environments for next-generation millimeter-wave (mmWave) networks. However, the prohibitive computational overhead of Channel State Information (CSI) estimation and the dimensionality explosion inherent in centralized optimization severely hinder practical large-scale deployments. To overcome these bottlenecks, we introduce a ``CSI-free" paradigm powered by a Hierarchical Multi-Agent Reinforcement Learning (HMARL) architecture to control mechanically reconfigurable reflective surfaces. By substituting pilot-based channel estimation with accessible user localization data, our framework leverages spatial intelligence for macro-scale wave propagation management. The control problem is decomposed into a two-tier neural architecture: a high-level controller executes temporally extended, discrete user-to-reflector allocations, while low-level controllers autonomously optimize continuous focal points utilizing Multi-Agent Proximal Policy Optimization (MAPPO) under a Centralized Training with Decentralized Execution (CTDE) scheme. Comprehensive deterministic ray-tracing evaluations demonstrate that this hierarchical framework achieves massive RSSI improvements of up to 7.79 dB over centralized baselines. Furthermore, the system exhibits robust multi-user scalability and maintains highly resilient beam-focusing performance under practical sub-meter localization tracking errors. By eliminating CSI overhead while maintaining high-fidelity signal redirection, this work establishes a scalable and cost-effective blueprint for intelligent wireless environments.