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.

翻译：可重构智能表面（RIS）的部署为多小区无线网络中的资源分配带来了新的挑战，特别是在基站间用户负载不均衡的情况下。本文将RIS视为必须在竞争基站间动态分配的共享基础设施，并采用同步升价拍卖机制解决该问题。为缓解小区间的性能失衡，我们提出了一种公平性感知的协作多智能体强化学习方法，使基站根据预期效用增益和相对服务质量调整其竞标策略。智能体的观测中融入了中心计算的性能依赖性公平指标，从而无需基站间直接通信即可实现隐式协调。仿真结果表明，所提框架能有效将RIS资源重新分配至性能较弱的小区，显著提升最差服务用户的速率，同时保持整体吞吐量。研究证明，面向公平性的RIS分配可通过协作学习实现，为未来无线网络中平衡效率与公平性提供了灵活工具。