Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors
Summary: arXiv:2604.05165v1 Announce Type: new
Abstract
Reconfigurable Intelligent Surfaces (RIS) have the 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.
Key Features of the Proposed Framework
The developed framework is structured to efficiently address the challenges associated with CSI estimation and centralized optimization. The following are the key features of this innovative approach:
- Two-Tier Neural Architecture: The control problem is decomposed into a high-level controller and low-level controllers, allowing for a more effective management of the reconfigurable surfaces.
- High-Level Controller: This component executes temporally extended, discrete user-to-reflector allocations, optimizing user experience over time.
- Low-Level Controllers: These controllers autonomously optimize continuous focal points utilizing Multi-Agent Proximal Policy Optimization (MAPPO) under a Centralized Training with Decentralized Execution (CTDE) scheme.
- Robust Performance: The system demonstrates impressive improvements in Received Signal Strength Indicator (RSSI) by up to 7.79 dB compared to centralized baselines.
- Scalability: The framework exhibits robust multi-user scalability, making it suitable for environments with multiple users.
- Resilience to Localization Errors: The system maintains high-fidelity beam-focusing performance even under practical sub-meter localization tracking errors.
Impact on Future Wireless Networks
This work establishes a scalable and cost-effective blueprint for intelligent wireless environments. By eliminating the CSI overhead while maintaining high-fidelity signal redirection, the proposed architecture opens up new avenues for the deployment of RIS in next-generation wireless networks. The ability to manage wave propagation effectively using spatial intelligence represents a significant leap forward in the optimization of communication networks.
As researchers continue to explore the capabilities of RIS in conjunction with advanced learning algorithms, the implications for both industrial and consumer applications are profound. The transition towards CSI-free methodologies promises to facilitate greater adaptability and efficiency in wireless communication systems, ultimately enhancing user experiences in an increasingly connected world.