This paper studies a multi-Intelligent Reflecting Surfaces (IRSs)-assisted wireless network consisting of multiple base stations (BSs) serving a set of mobile users. We focus on the IRS-BS association problem in which multiple BSs compete with each other for controlling the phase shifts of a limited number of IRSs to maximize the long-term downlink data rate for the associated users. We propose MDLBI, a Multi-agent Deep Reinforcement Learning-based BS-IRS association scheme that optimizes the BS-IRS association as well as the phase-shift of each IRS when being associated with different BSs. MDLBI does not require information exchanging among BSs. Simulation results show that MDLBI achieves significant performance improvement and is scalable for large networking systems.

翻译：本文研究多智能反射表面(IRS)辅助无线网络,由多基站组成,为一组移动用户提供服务,我们着重探讨IRS-BS联系问题,即多个IRS联系问题相互竞争,以控制数量有限的IRS的分阶段转移,最大限度地提高相关用户的长期下行链路数据率。我们建议MDLBI,一个基于BS-IRS的多剂深层强化学习BS-IRS联系计划,优化BS-IRS联系,以及每个IRS在与不同BS联系时的分阶段转移。MDLBI不需要BS之间交流信息。模拟结果表明MDLBI工作取得了显著的绩效改进,对于大型联网系统来说是可扩缩的。