Bayesian Channel Estimation in Multi-User Massive MIMO With Extremely Large Antenna Array

We investigate wideband uplink channel estimation for a multi-user (MU) multiple-input single-output (MISO) OFDM system, in which the base station (BS) is equipped with an extremely large antenna array (ELAA). The existing compressive sensing massive multiple-input multiple-output (MIMO) channel est...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2021, Vol.69, p.5463-5478
Main Authors: Zhu, Yifan, Guo, Huayan, Lau, Vincent K. N.
Format: Article
Language:English
Subjects:
Algorithms
Antenna arrays
Antennas
Bayes methods
Bayesian analysis
Channel estimation
Extremely large antenna array
Hidden Markov models
Markov chains
Massive MIMO
Message passing
MIMO communication
MISO (control systems)
Scattering
Sparsity
Statistical inference
structured sparsity
Visibility
Wave fronts
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Summary:We investigate wideband uplink channel estimation for a multi-user (MU) multiple-input single-output (MISO) OFDM system, in which the base station (BS) is equipped with an extremely large antenna array (ELAA). The existing compressive sensing massive multiple-input multiple-output (MIMO) channel estimation approach with a traditional sparsity promoting prior model becomes invalid in the ELAA scenario due to the spatial non-stationary effects caused by the spherical wavefront and visibility region (VR) issue. We therefore propose a new structured prior with the Hidden Markov Model (HMM) to promote the structured sparsity of the spatial non-stationary ELAA channel. Based on this, a Bayesian inference problem on the posterior of the ELAA channel coefficients is formulated. In addition, we propose the turbo orthogonal approximate message passing (Turbo-OAMP) algorithm to achieve a low-complexity channel estimation. Comprehensive simulations verify that the proposed algorithm has supreme performance under spatial non-stationary ELAA channels compared to various state-of-the-art baselines.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2021.3114999