Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems

Chen JI
Haijun FU

IEICE TRANSACTIONS on Communications   Vol.E105-B    No.5    pp.600-607
Publication Date: 2022/05/01
Publicized: 2021/11/11
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2021EBP3064
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
channel estimation,  massive multiple-input multiple-output (MIMO),  variational Bayesian inference (VBI),  temporal correlation,  off-grid refining,  

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This paper proposes a low-complexity variational Bayesian inference (VBI)-based method for massive multiple-input multiple-output (MIMO) downlink channel estimation. The temporal correlation at the mobile user side is jointly exploited to enhance the channel estimation performance. The key to the success of the proposed method is the column-independent factorization imposed in the VBI framework. Since we separate the Bayesian inference for each column vector of signal-of-interest, the computational complexity of the proposed method is significantly reduced. Moreover, the temporal correlation is automatically uncoupled to facilitate the updating rule derivation for the temporal correlation itself. Simulation results illustrate the substantial performance improvement achieved by the proposed method.