移动网络智能反射面-基站关联优化
Optimizing Intelligent Reflecting Surface-Base Station Association for Mobile Networks
摘要：
本文研究了一个由多个基站组成的多智能反射面（IRSs）辅助的无线网络，该网络服务于一组移动用户。我们专注于IRS-BS关联问题，其中多个BS相互竞争，以控制有限数量的IRS的相移，以最大限度地提高长期下行链路数据率的相关用户。我们提出了MDLBI，一种基于多智能体深度强化学习的BS-IRS关联方案，该方案优化了BS-IRS关联以及每个IRS与不同BS关联时的相移。MDLBI不需要在BS之间交换信息。仿真结果表明，MDLBI实现了显着的性能改善，是可扩展的大型网络系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
