Journal
IEEE COMMUNICATIONS LETTERS
Volume 23, Issue 4, Pages 672-675Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2019.2897995
Keywords
Massive MIMO; mmWave; off-grid; sparse Bayesian learning
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Funding
- National Natural Science Foundation of China [61871065, 61871455]
- open research fund of National Mobile Communications Research Laboratory, Southeast University [2018D03]
- National Key Research and Development Program of China [2017YFB0503403]
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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.
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