Cooperative Deep Reinforcement Learning for Fair RIS Allocation

TL;DR

This paper introduces a fairness-aware cooperative reinforcement learning framework for dynamic RIS resource allocation in multi-cell wireless networks, improving user fairness without sacrificing overall throughput.

Contribution

It proposes a novel multi-agent reinforcement learning approach that incorporates a fairness indicator for implicit coordination among base stations in RIS allocation.

Findings

Significantly improves the rates of worst-served users.

Effectively redistributes RIS resources toward weaker cells.

Maintains overall network throughput.

Abstract

The deployment of reconfigurable intelligent surfaces (RISs) introduces new challenges for resource allocation in multi-cell wireless networks, particularly when user loads are uneven across base stations. In this work, we consider RISs as shared infrastructure that must be dynamically assigned among competing base stations, and we address this problem using a simultaneous ascending auction mechanism. To mitigate performance imbalances between cells, we propose a fairness-aware collaborative multi-agent reinforcement learning approach in which base stations adapt their bidding strategies based on both expected utility gains and relative service quality. A centrally computed performance-dependent fairness indicator is incorporated into the agents' observations, enabling implicit coordination without direct inter-base-station communication. Simulation results show that the proposed framework effectively redistributes RIS resources toward weaker-performing cells, substantially improving the rates of the worst-served users while preserving overall throughput. The results demonstrate that fairness-oriented RIS allocation can be achieved through cooperative learning, providing a flexible tool for balancing efficiency and equity in future wireless networks.