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）是一种极具前景的技术，它能够通过智能地操控无线信道来提升无线通信系统的性能。本文研究了在空分多址（SDMA）下行链路多用户多输入单输出（MISO）信道中的和速率最大化问题。该问题面临两大挑战：一是大量RIS单元导致的高维度性，二是获取完整信道状态信息（CSI）的困难——这在现有文献的许多算法中被假定为已知。为此，我们提出了一种混合机器学习方法，在基站（BS）处采用加权最小均方误差（WMMSE）预编码器，并设计了一种专用神经网络（NN）架构RISnet用于RIS配置。RISnet具有良好的可扩展性，可优化多达1296个RIS单元，且仅需以16个RIS单元的部分CSI作为输入。我们证明，在通过射线追踪仿真得到的几何信道模型中，该方法能够以较低的信道估计需求实现高性能。无监督学习使RISnet能够自主找到优化的RIS配置。数值结果表明，训练后的模型能以较低计算复杂度配置RIS，显著优于基线方法，并且能够兼容离散相移。