Channel Estimation in Mobile Scenarios for RIS-Assisted Terahertz Systems

In response to the demand for high data rates in 6th generation wireless systems, terahertz (THz) communications assisted by reconfigurable intelligent surface (RIS) have garnered significant attention in recent years. To fully harness the potential of RIS-assisted terahertz systems, the accurate acquisition of channel information is crucial. In mobile scenarios, estimating channel information presents considerable challenges owing to the restricted signal processing capabilities of passive elements in RIS. In this paper, we propose a channel estimation method for RIS-assisted terahertz communication systems. The cascaded channels are represented using a state-space model, and we utilize the Kalman filter, which fully leverages the temporal correlation of the channel, for channel estimation. Furthermore, the optimal reflection coefficient matrix is obtained based on the MMSE criterion. Numerical results demonstrate that the proposed scheme in this paper displays superior performance in Normalized Mean Squared Error (NMSE) compared to conventional channel estimation methods.