Active IRS-Assisted MIMO Channel Estimation and Prediction

This paper considers a wireless network assisted by an intelligent reflecting surface (IRS) to enhance data transmission between the base station and mobile users. Our objective is to estimate and predict the user-IRS channels by exploiting a small number of sparsely distributed active elements with a low pilot overhead. The Hermitian and Toeplitz properties of the data covariance matrices are used to perform covariance matrix interpolation for enhanced estimation of the time-varying user-IRS multipath channels, and a machine learning-based channel predictor is developed to predict the channels based on prior channel estimates so as to shorten the required training pilot signals and enhance the transmission data rate. Simulation results verify the effectiveness of the proposed method for accurate channel estimation and prediction.