论文标题
RIS AID的多源误差系统的基于统计CSI的光束成型，使用深度强化学习
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning
论文作者
论文摘要
该论文使用统计通道状态信息（S-CSI）提出了一种连接算法，用于可重构智能表面（RIS），用于多途Miso无线通信。我们使用了S-CSI，这是级联通道的长期平均值，而不是在大多数现有作品中使用的瞬时CSI。通过这种方法，通道估计的开销大大降低了。我们提出了一种近端策略优化（PPO）算法，该算法是众所周知的基于参与者的强化学习（RL）算法，以解决优化问题。为了测试该算法的疗效，介绍了对关键系统参数的评估，包括RICIAN因素和RIS位置，对用户的可实现总和率。
The paper presents a joint beamforming algorithm using statistical channel state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for multiuser MISO wireless communications. We used S-CSI, which is a long-term average of the cascaded channel as opposed to instantaneous CSI utilized in most existing works. Through this method, the overhead of channel estimation is dramatically reduced. We propose a proximal policy optimization (PPO) algorithm which is a well-known actor-critic based reinforcement learning (RL) algorithm to solve the optimization problem. To test the efficacy of this algorithm, simulation results are presented along with evaluations of key system parameters, including the Rician factor and RIS location, on the achievable sum rate of the users.
