A Two-Stage Refinement Channel Estimation Scheme in Multi-RIS-Assisted Multi-User Communication Systems

Channel estimation is a pivotal challenge in reconfigurable-intelligent-surface-assisted (RIS-assisted) wireless communications. Despite the existence of numerous channel estimation methods, there remains a significant gap in addressing channel estimation problems in multi-user, multi-RIS, and multipath scenarios. In this paper, a two-stage channel refinement scheme based on both data-level and feature-level refinement of the channel is proposed. The first-stage refinement achieves channel estimation under low-bit RIS phase shifting conditions through joint design of the RIS phase shift matrices and the transmitted user pilot signals, thereby preliminarily enhancing the channel estimation accuracy. Subsequently, the second-stage further refines the estimation results via feature-level channel decomposition and reconstruction, effectively addressing the challenges posed by multi-user, multi-RIS, and multipath channels with further performance improvement. Moreover, Cramér-Rao lower bound (CRLB) for channel estimation is derived. Theoretical analysis and simulation results verify the effectiveness of the proposed scheme.