Two-Stage Channel Estimation in Multi-User Wireless Systems for Double Reconfigurable Intelligent Surface With Hardware Impairments

This paper investigates the uplink channel estimation for a double reconfigurable intelligent surface (RIS) assisted system, considering RIS hardware impairments. To address the performance degradation of conventional pilot-based channel estimation schemes due to impaired training pilots caused by RIS hardware impairments, a Bayesian posterior probability (BPP) based estimation algorithm is proposed to estimate the hardware impairments, which are then mitigated. Subsequently, this paper proposes a two-stage channel estimation scheme. In the first stage, leveraging the fixed mode of RIS1 and the dynamically adjustable mode of RIS2, the uplink reflection channel from a typical user to the base station is estimated. In the second stage, using the results from the first stage and the strong correlation among user channels, the reflection channels of other users are estimated with low overhead. Simulation results show that the proposed scheme has good estimation performance.