期刊
IEEE COMMUNICATIONS LETTERS
卷 25, 期 6, 页码 2004-2008出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2021.3065751
关键词
Massive MIMO; channel state information (CSI) feedback; randomized matrix approximation; low rank
资金
- National Natural Science Foundation of China (NSFC) [U1805262]
- Ministry of Industry and Information Technology of P.R. China Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks
This paper proposes a novel CSI feedback method with low complexity and high precision to accurately recover CSI. The method exploits the low-rank characteristic of a large channel matrix by approximating it with small sub-matrices.
Massive multiple-input multiple-output (MIMO) is regarded as one enabling technique to improve channel capacity and energy/spectrum efficiency of 5G communications. To attain such potential benefits, accurate channel information is critical to the transmitter, which yet remains a challenging task for frequency division duplexing (FDD) systems, i.e., the channel state information (CSI) feedback tends to be resource-demanding especially for massive MIMO communications. In this work, we propose a novel CSI feedback method with low complexity and high precision, which is inspired by randomized matrix approximation. Our approach exploits the inherent low-rank characteristic of a large channel matrix, and approximates it by small sub-matrices which are then reported to transmitter to recover a CSI matrix. Theoretical bounds of the recovered CSI in both error-free and error cases are derived. Simulation results demonstrate our method could recover CSI accurately via an extremely low complexity and yet achieve a largely reduced compression ratio (or feedback overhead), compared to other schemes. It thus has the great potential in the emerging massive MIMO FDD communications.
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