论文标题
基于RIS辅助的MISO系统的基于深钢筋学习的关节主动和被动横梁形式设计
Deep Reinforcement Learning based Joint Active and Passive Beamforming Design for RIS-Assisted MISO Systems
论文作者
论文摘要
由于低成本和可控性具有独特的优势，可重构智能表面（RIS）是一个有前途的候选人，可以解决毫米波（MMWave）通信系统中的阻塞问题，因此近年来引起了广泛的关注。但是，由于高计算复杂性和无线环境的动态变化，关节主动梁的形成和被动横梁的设计是一项艰巨的任务。在本文中，我们考虑了RIS辅助的多用户多输入单输出（MU-MISO）MMWAVE系统，并旨在开发基于深的增强算法（DRL）算法，以在RIS侧共同设计基本站（BS）侧和无线电束形的主动混合光束器。通过采用先进的软批评者（SAC）算法，我们提出了一种基于最大熵的DRL算法，该算法可以探索比确定性策略更多的随机策略，以同时设计主动类似物的预编码器和被动式光束形式。然后，通过最小平方误差（MMSE）方法确定数字编码器。实验结果表明，与常规优化算法和DRL算法相比，我们提出的SAC算法可以实现更好的性能。
Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.
