A Sparse-Based Channel Estimation Scheme for XL-RIS-Assisted Wireless Communications With Low Pilot Overheads

The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region to meet high requirements of sixth-generation communication systems. In XL-RIS-assisted scenarios, users transmit signal through spherical-wavefront near-field channel, where the channel estimation presents great challenges. Additionally, XL-RIS elements are prone to failure due to accidental damages or blockages, which further complicates the channel state. To address these issues, we propose a three-stage signal-assisted sparse representation-based channel estimation (SA-SRCE) scheme to robustly recover near-field channel and diagnose RIS failure state with low pilot overhead. The first stage utilizes a double domain filter (DDF) to eliminate noise based on sparse representation in the angle and polar domains. Then, based on the filtered signal, a failure-aware orthogonal matching pursuit (FA-OMP) algorithm is proposed to iteratively reconstruct the channel, as well as eliminate the perturbation invoked by RIS element failures. Moreover, considering the limited availability of pilot, we further exploit the information of compressed signal and propose the signal-assisted failure-aware double-sparsity OMP (FADS-OMP) algorithm as the third stage to improve the estimation performance in FA-OMP. The effectiveness and robustness of our proposed scheme is validated through theoretical analysis and extensive simulations.