期刊
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
卷 6, 期 1, 页码 208-220出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGCN.2022.3146188
关键词
Channel estimation; Sparse matrices; Massive MIMO; Downlink; Wireless communication; Estimation; Training; Intelligent reflecting surface (IRS); cascaded channel estimation; sparse representation; approximate message passing (AMP); massive MIMO
资金
- National Natural Science Foundation of China (NSFC) [62071206]
- Jiangsu Graduate Research and Practice Innovation Program [KYCX21_3366]
- NSFC [62171141, 61971289]
This paper investigates the downlink channel estimation problem for IRS-assisted massive MIMO systems. A new sparse recovery problem formulation is proposed, utilizing the sub-surface idea to reduce computational complexity. A hybrid approximate message passing framework is employed to recover the row-sparse matrix, and the superiority of the proposed method is verified through numerical simulations.
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.
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