Correlation-Based Channel Estimation Scheme for Double-IRS-Assisted MIMO Systems

Distributed intelligent reflecting surfaces (IRSs) are promising to be deployed in urban environments for enhanced performance in signal coverage, system capacity and delay without additional consumption of bandwidth and transmit power. However, obtaining the individual channel state information (CSI) of the cascaded path through each IRS is challenging, and most existing estimation schemes usually treat all the distributed IRSs as a virtual entity and do not consider the individual CSI of each IRS, which restricts full exploration on IRS-assisted systems and design flexibility. In this paper, for distributed double-IRS-assisted communication systems with multiple users, a novel channel estimation scheme is proposed, through which the individual CSI of the double IRSs is obtained simultaneously, and the performance in accuracy and delay is improved greatly as well. Specifically, leveraging an optimization-based framework with the maximum a posteriori (MAP) approach, we first estimate the overall CSI by turning all the IRS elements on. Then, the individual CSI of each IRS is derived by employing the unrelated characteristics among the elements of different IRSs, which is more efficient and accurate compared to the conventional ones. Numerical simulations are presented to show the effectiveness and efficiency of the proposed channel estimation scheme.