In this paper, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributions, we consider long-term CSI-based transmission design, where both the beamforming vectors at the base station (BS) and the phase shifts at the RIS are designed based on long-term CSI, which can significantly reduce the channel estimation overhead. Due to the lack of explicit ergodic data rate expression, we propose a novel deep deterministic policy gradient (DDPG) based algorithm to solve the optimization problem, which was trained by using the channel vectors generated in an offline manner. Simulation results demonstrate that the achievable net throughput is higher than that achieved by the conventional instantaneous-CSI based scheme when taking the channel estimation overhead into account.

翻译：在本文中,我们研究了可重新配置智能表面(RIS)辅助多用户通信网络的传输设计。与大多数现有贡献不同的是,我们考虑以CSI为基础的长期传输设计,即基站的波束成形矢量和RIS的分阶段转移都是以长期的CSI为基础设计的,这可以大大减少频道估计间接费用。由于缺乏明确的异地数据率表达,我们建议采用一种新的基于深度确定性政策梯度的算法来解决优化问题,该算法是通过使用离线生成的通道矢量来培训的。模拟结果显示,考虑到频道估计间接费用,可实现的净吞吐量高于传统的即时 CSI计划所实现的净吞吐量。