Holographic-Inspired Channel Estimation for Reconfigurable Intelligent Surface Systems

Hardware-efficient channel estimation in reconfigurable intelligent surface (RIS)-assisted systems is challenging due to the passive nature of RIS elements and the large pilot overhead associated with massive arrays. This letter proposes a novel holographic-inspired channel estimation framework that significantly reduces training complexity. The proposed methods exploit the integration of two time-delayed signals measured at the RIS to resolve ambiguities inherent in conventional real-valued channel responses. Simulation results show that our proposed framework improves estimation accuracy as RIS size, oversampling factors, and the number of repeated measurements increase. Furthermore, it outperforms recently developed algorithms, especially in low signal-to-noise ratio (SNR) conditions, while simultaneously reducing the pilot overhead.