Multiple access techniques are cornerstones of wireless communications. Their performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize MA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS. In particular, we design a dedicated neural network (NN) architecture RISnet with good scalability and desired symmetry. Moreover, we combine ML-enabled RIS configuration and analytical precoding at BS since there exist analytical precoding schemes. Furthermore, we propose another variant of RISnet, which requires the channel state information (CSI) of a small portion of RIS elements (in this work, 16 out of 1296 elements) if the channel comprises a few specular propagation paths. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and symmetry.

翻译：多址接入技术是无线通信的基石，其性能取决于信道特性，而可重构智能表面（RIS）可改善信道特性。本文联合优化基站（BS）的多址接入预编码与RIS配置，以解决RIS单元间的互耦效应、超过1000个RIS单元的可扩展性以及信道估计等难题。首先推导了考虑互耦效应的RIS辅助信道模型，随后提出一种无监督机器学习（ML）方法优化RIS。具体而言，我们设计了专用的神经网络（NN）架构RISnet，兼具良好的可扩展性与期望的对称性。同时，鉴于存在解析预编码方案，我们将基于ML的RIS配置与BS的解析预编码相结合。进一步，提出了RISnet的另一种变体——当信道包含少量镜面传播路径时，该变体仅需部分RIS单元（本文中为1296个单元中的16个）的信道状态信息（CSI）。更广泛而言，本文是早期将ML技术与通信领域知识结合用于神经网络架构设计的工作。与通用ML相比，问题导向型ML能够实现更高性能、更低复杂度及更好的对称性。