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.

翻译：在大规模可重构智能超表面（RIS）通信系统中，精确获取信道状态信息（CSI）具有挑战性。考虑实际RIS配置中仅有少数反射单元作为锚点估计CSI的情况，称之为部分CSI。为提升对部分CSI的鲁棒性和RIS网络的可扩展性，本文提出一种面向RIS辅助多用户系统的无监督学习速率分割多址接入（RSMA）方案。具体而言，RISnet这一从部分观测中推断完整CSI的神经网络架构被采用，并与低复杂度RSMA预编码器集成。通过部分CSI构建有效信道特征，原始含信道信息的单元经RISnet扩展后成为新锚点。数值结果表明，在确定性射线追踪信道条件下，所提方案性能逼近采用完整RIS的CSI情景。当训练过程中信道不确定性增加时，RSMA可增强RISnet的鲁棒性，显著缓解性能损失。