Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.
翻译:重新配置的智能表面(RIS)被认为是一种极有希望的技术,可以解决隔断的毫米波(mmWave)通信问题,因为它能够建立一个可重新配置的无线传播系统。在本文中,我们侧重于一个由多基站(BS-RIS-UE)组成的由多基站(BS-AW)组成的、为一套用户设备(UES)服务的多基站(BS-LIS-UE)组成的系统。考虑到BS-RIS-UE的关联问题,它决定RIS应该帮助哪个BS和UES,我们共同优化BS-RIS-UE协会,被动地在RIS形成系统,以最大限度地提高系统的总和率。为了解决这一棘手的非电离子问题,我们建议采用软式的动作-加速学习(SAC)深强化式的基于联合波束式和BS-RIS-UE协会设计算法,通过使用较不先进的信息与环境互动,避免与当地最佳解决方案相适应,同时将政策信息纳入最大化的模拟结果。模拟结果表明,拟议的SAC-DRDRL算算算法能够与基准计划取得显著的成绩。