Scalable Design for RIS-Assisted Multi-User Downlink System Empowered by RSMA under Partial CSI

TL;DR

This paper introduces an unsupervised learning-based RSMA scheme for RIS-assisted multi-user systems that effectively handles partial CSI, improving scalability and robustness in large-scale RIS networks.

Contribution

It proposes RISnet, a neural network that infers full CSI from partial observations, integrated with a low-complexity RSMA precoder for enhanced RIS system performance.

Findings

RISnet approximates full CSI performance under deterministic channels.

RSMA improves robustness against channel uncertainty.

The scheme mitigates performance loss with partial CSI.

Abstract

In large-scale reconfigurable intelligent surface (RIS) communication systems, the precise acquisition of channel state information (CSI) is challenging. Consider a practical RIS configuration where only a few reflective elements serve as anchors to estimate CSI, which are termed partial CSI. To improve the robustness against partial CSI and the scalability of RIS networks, this paper proposes an unsupervised learning-based rate-splitting multiple access (RSMA) scheme for RIS-assisted multi-user systems. Specifically, RISnet, a neural network architecture designed to infer full CSI from partial observations, is employed and integrated with a low-complexity RSMA precoder. Effective channel features are constituted from partial CSI, and the original elements with channel information contribute to new anchors after expansion in RISnet. Numerical results demonstrate that the proposed scheme approximates the performance with a full CSI of RIS under deterministic raytracing channel conditions. When channel uncertainty increases during training, RSMA has been shown to enhance RISnet robustness, significantly mitigating performance loss.