Sparse Channel Estimation for Intelligent Reflecting Surface Assisted Massive MIMO Systems
Lei Zhou, Jisheng Dai, Weichao Xu, Chunqi Chang
Abstract
Intelligent reflecting surface (IRS) has emerged as a promising technology for improving the spectrum and energy efficiency of next-generation wireless communications. Accurately acquiring channel state information (CSI) of the IRS-assisted wireless system is an essential task for reaping the passive beamforming gain of IRS. However, it is quite difficult to directly obtain the CSI of the IRS-assisted wireless system due to the inability of signal processing at the IRS. In this paper, we investigate the downlink channel estimation problem for IRS-assisted massive MIMO systems. We first present a new sparse recovery problem formulation for the cascaded downlink channel estimation, which exploits the sparsity of massive MIMO channels at the base station (BS) side and adopts the sub-surface idea to significantly reduce computational complexity and training overhead. In this case, the corresponding sparse signal recovery problem exhibits a row-sparse feature but is additionally affected by a coupling matrix. It is challenging to recover the row-sparse matrix with the traditional sparse representation methods, as the elements in the row-sparse matrix are highly coupled with each other. To meet the challenge, we employ a hybrid approximate message passing (AMP) framework for the cascaded downlink channel estimation, which consists of a part of expectation propagation approximation (EPA) and a part of two-level generalized approximate message passing (GAMP). We illustrate that combining EPA and GAMP into a hybrid framework can make up for their respective shortcomings. Numerical simulation results verify the superiority of the proposed method.