Deep Learning-Enabled Energy-Efficient Joint CSI Estimation and Phase Shift Prediction in Large-Scale RIS-Assisted NOMA Systems

Abstract

In large-scale reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) systems, achieving energy-efficient communication while obtaining full channel state information (CSI) is challenging due to prohibitive pilot overhead. The joint CSI estimation and RIS phase shift prediction pose a tightly coupled challenge between performance and energy consumption. In this paper, we propose an energy-efficient and deep learning (DL)–based end-to-end framework that jointly performs CSI estimation and phase shift prediction for a large-scale RIS-assisted NOMA system, leveraging only partial CSI obtained from a small subset of active RIS elements (6%). The proposed framework integrates a DL model termed DSRNetV2 for CSI estimation and a lightweight phase shift prediction network termed PhaseNet. Both models are jointly trained using an energy-efficient dynamic hybrid learning strategy, which first minimizes the estimation error and then maximizes the system sum rate. Simulation results demonstrate that the proposed hybrid learning approach outperforms the fixed-weight strategy by 32% in throughput and 37% in energy efficiency, and exceeds the MSE-only training by 59% in throughput and 82% in energy efficiency, while maintaining the fairness over 99% for the weaker user. These results confirm that the proposed framework achieves accurate channel recovery, efficient phase shift prediction, and high energy efficiency, supporting a sustainable RIS-NOMA design for future green 6G systems.