Cooperative Deep Reinforcement Learning for Fair RIS Allocation
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-de...
| Authors | Martin Mark Zan, Stefan Schwarz |
|---|---|
| Primary category | cs.NI |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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