Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 67, Issue 5, Pages 1251-1266Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2018.2890058
Keywords
Millimeter wave communication; MIMO; channel estimation; sparsity; sparse Bayesian learning; hierarchical Bayesian Kalman filter; Cramer-Rao bound
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Funding
- Qualcomm, India
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This paper develops schemes for block-sparse channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems that exploit the spatial sparsity inherent in such channels. Initially, a novel sparse Bayesian learning (SBL) based block-sparse channel estimation technique is developed for a mm Wave hybrid MIMO system with multiple measurement vectors, which overcomes the shortcomings of the existing orthogonal matching pursuit-based framework. This is subsequently extended to a temporally correlated block-sparse mm Wave MIMO channel. Further, an online recursive hierarchical Bayesian Kalman Filter is developed for the estimation of a time-selective mm Wave MIMO channel. Bayesian Cramer-Rao bounds are also derived for the proposed static and time-selective mmWave MIMO channel estimation schemes followed by precoder/combiner design employing the SBL-based imperfect channel estimates. Simulation results are presented to demonstrate the improved performance of the proposed SBL-based channel estimation techniques in comparison to the popular OMP-based scheme proposed recently.
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