[2603.25572] Cooperative Deep Reinforcement Learning for Fair RIS Allocation

Computer Science > Networking and Internet Architecture arXiv:2603.25572 (cs) [Submitted on 26 Mar 2026] Title:Cooperative Deep Reinforcement Learning for Fair RIS Allocation Authors:Martin Mark Zan, Stefan Schwarz View a PDF of the paper titled Cooperative Deep Reinforcement Learning for Fair RIS Allocation, by Martin Mark Zan and 1 other authors View PDF HTML (experimental) 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...