RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach

TL;DR

This paper presents a novel machine learning-based approach for optimizing RIS-assisted NOMA systems with partial CSI, addressing mutual coupling and scalability issues, and integrating domain knowledge into neural network design.

Contribution

It introduces RISnet, a neural network architecture inspired by communication domain knowledge, for joint optimization of BS precoding and RIS configuration with low CSI requirements.

Findings

High scalability to over 1000 RIS elements

Low CSI input requirement

Effective handling of mutual coupling

Abstract

Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge in communication. Compared to state-of-the-art, the proposed approach combines analytical optimal BS precoding and ML-enabled RIS, has a high scalability to control more than 1000 RIS elements, has a low requirement for channel state information (CSI) in input, and addresses the mutual coupling between RIS elements. Beyond the considered problem, this work is an early contribution to domain knowledge enabled ML, which exploit the domain expertise of communication systems to design better approaches than general ML methods.