论文标题
基于DRL的联合波束形成和BS-RIS-EU协会设计用于RIS辅助MMWave网络
DRL-based Joint Beamforming and BS-RIS-UE Association Design for RIS-Assisted mmWave Networks
论文作者
论文摘要
可重构智能表面（RIS）被认为是一种非常有前途的技术，可以解决毫米波（MMWave）通信的阻塞问题，因为它能够建立可重构的无线传播。在本文中，我们专注于由多个基站（BSS）组成的RIS辅助MMWave通信网络，该网站（BSS）为一组用户设备（UES）组成。考虑到BS-RIS-UE关联问题，该问题确定RIS应该有助于BS和UES，我们在RIS处联合优化BS-RIS-EU关联和被动光束，以最大程度地提高系统的总和率。为了解决这个棘手的非凸问题，我们提出了一种软演员批判性（SAC）深度加固学习（DRL）的关节波束成形和BS-RIS-UE协会设计算法，可以通过更少的事先信息与环境相互作用，并避免与当地的最佳解决方案相互作用，并通过将最佳的解决方案与最大程度的最佳解决方案进行互动，并避免使用最佳的最佳解决方案。模拟结果表明，与基准方案相比，提出的SAC-DRL算法可以实现显着的性能提高。
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.
