The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wireless channels. In this paper, we consider the sum-rate maximization problem in a downlink multi-user multi-input-single-output (MISO) channel via space-division multiple access (SDMA). Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature. Instead, we propose a hybrid machine learning approach using the weighted minimum mean squared error (WMMSE) precoder at the base station (BS) and a dedicated neural network (NN) architecture, RISnet, for RIS configuration. The RISnet has a good scalability to optimize 1296 RIS elements and requires partial CSI of only 16 RIS elements as input. We show it achieves a high performance with low requirement for channel estimation for geometric channel models obtained with ray-tracing simulation. The unsupervised learning lets the RISnet find an optimized RIS configuration by itself. Numerical results show that a trained model configures the RIS with low computational effort, considerably outperforms the baselines, and can work with discrete phase shifts.

翻译：可重构智能表面（RIS）是一种能够通过智能调控无线信道提升通信系统性能的前沿技术。本文针对下行链路多用户多输入单输出（MISO）信道，考虑基于空分多址（SDMA）的系统和速率最大化问题。该问题面临两大挑战：一是RIS单元数量庞大导致的高维度问题，二是现有算法普遍假设可获取完整信道状态信息（CSI）的困难性。为此，我们提出一种混合机器学习方法，采用基站（BS）端的加权最小均方误差（WMMSE）预编码器，并设计专用神经网络（NN）架构——RISnet用于RIS配置。RISnet具有良好可扩展性，可优化多达1296个RIS单元，且仅需16个RIS单元的部分CSI作为输入。研究表明，针对射线追踪仿真获得的几何信道模型，该方法在信道估计要求较低的条件下仍能实现高性能。通过无监督学习，RISnet可自主寻找最优RIS配置。数值结果表明，训练后的模型能以较低计算开销完成RIS配置，显著优于基准方案，并可兼容离散相位控制。