用协作强化学习公平分配智能反射面，提升弱信号用户速率。
Cooperative Deep Reinforcement Learning for Fair RIS Allocation
- 基站在拍卖中自适应竞价，结合收益与服务质量
- 弱覆盖小区的用户速率显著提升，整体吞吐量不变
- 无需通信即可实现公平性，适合负载不均的蜂窝网络
部署可重构智能表面（RIS）为多小区无线网络资源分配带来新挑战，尤其在基站间用户负载不均时。本文将RIS视为共享基础设施，采用同步升价拍卖机制进行动态分配。为缓解小区间性能差异，提出一种面向公平性的协作多智能体强化学习方法，各基站根据预期收益增益和相对服务品质调整竞价策略。通过中心计算的性能相关公平性指标融入智能体观测，实现无需直接通信的隐式协调。仿真结果表明，该框架能有效将RIS资源向表现较差的小区倾斜，显著提升最差服务用户的速率，同时保持整体吞吐量稳定。结果证明，通过协作学习可实现面向公平的RIS分配，为未来无线网络中的效率与公平性平衡提供灵活工具。
原文摘要 · Abstract (English)
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.
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