Semi-Passive Elements Assisted Channel Estimation for Intelligent Reflecting Surface-Aided Communications

In this paper, we propose a novel semi-passive elements-aided channel estimation framework for intelligent reflecting surface (IRS), where a small portion of IRS reflecting elements are able to process the received signal for facilitating the channel estimation. Specifically, the BS-IRS channel is estimated by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), while the user-IRS channels are estimated by combining the use of total least square (TLS) ESPRIT and multiple signal classification (MUSIC) methods. The required training time of the proposed channel estimation scheme is irrelevant to the number of IRS reflecting elements, thus substantially reducing the training overhead. Simulation results show the great advantages of our proposed scheme over both the conventional compressed sensing (CS)-based channel estimation and cascaded channel estimation schemes.