IRS-based aided channel estimation method for wireless communication

In Intelligent Reflective Surface (IRS)-assisted wireless communication systems, the channel estimation aspect is indispensable. Especially when dealing with mobile users to enhance the signal transmission efficiency, the time-varying channel presented is not linear due to the mobile state of the users, which brings challenges to the channel estimation. For this reason, it is necessary to construct a time-varying channel model that meets this situation and improve the traditional channel estimation algorithm based on this model. Specifically, the adopted improvement method is able to predict the state of the next moment with the help of the channel state response of the previous moment. Through the improved Kalman filtering algorithm, the channel state is predicted based on the time correlation, and the iterative method of the algorithm is used to obtain the accurate time-varying coefficients of the time-varying channel, so as to effectively solve the tracking problem of the parameters in the time-varying channel. Finally, according to the obtained parameters, the compressed sensing algorithm is used to realize the recovery and reconstruction of the initial signal, so as to reach the high-precision channel estimation results. After simulation experiments, the proposed scheme shows more significant performance advantages than the traditional least squares algorithm and the minimum mean square error algorithm.