Channel Estimation Method Based on Training Sequence Optimization in Mobility Scenarios

In wireless communication systems assisted by Reconfigurable Intelligent Surface (RIS), Channel Situation Information (CSI) is the prerequisite and foundation for effectively regulating beamforming. A training sequence optimization channel estimation method based on Kalman filtering is proposed for RIS assisted MISO communication systems under time-varying conditions. Firstly, the Linear Minimum Mean Squared Error (LMMSE) method is used to estimate the CSI of the cascaded channel. Secondly, based on the obtained CSI, the reflection matrix of RIS and the training sequence transmitted by the base station were jointly optimized to minimize the mean square error of the original channel estimation. Finally, in order to fully utilize the temporal correlation of the channel, prior information of the channel was considered, and Kalman filter (KF) was adopted to further improve the accuracy of channel estimation. The simulation results demonstrate that the proposed channel estimation method has better performance compared to various benchmark methods.