Massive MIMO Channel Estimation Over the mmWave Systems Through Parameters Learning

In this letter, we formulate an off-grid channel model to characterize spatial sample mismatching in the discrete Fourier transform (DFT) based massive multiple-input-multiple-output (MIMO) channel estimation over the millimeter-wave (mmWave) band. Then, we decompose the off-grid mmWave massive MIMO...

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Bibliographic Details
Published in:IEEE communications letters 2019-04, Vol.23 (4), p.672-675
Main Authors: Shao, Weidong, Zhang, Shun, Zhang, Xiushe, Ma, Jianpeng, Zhao, Nan, Leung, Victor C. M.
Format: Article
Language:English
Subjects:
Antenna arrays
Bayesian analysis
Channel estimation
Channel models
Coherence
Computer simulation
Discrete Fourier transforms
Fourier transforms
Machine learning
Massive MIMO
Mathematical models
Millimeter waves
MIMO communication
mmWave
off-grid
Parameter estimation
sparse Bayesian learning
Training
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Summary:In this letter, we formulate an off-grid channel model to characterize spatial sample mismatching in the discrete Fourier transform (DFT) based massive multiple-input-multiple-output (MIMO) channel estimation over the millimeter-wave (mmWave) band. Then, we decompose the off-grid mmWave massive MIMO channel estimation into the learning of model parameters and virtual channel estimation. Specifically, an expectation maximization (EM) based sparse Bayesian learning framework is first developed to learn the model parameters, such as bias parameters and spatial signatures, with unknown noise. With the learned model parameters, we resort to the linear minimum mean square error method to estimate the instantaneous virtual channel with less pilot overhead. Finally, we corroborate the validity of the proposed method through numerical simulations.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2897995