Super-resolution spatial channel covariance estimation for hybrid precoding in mmWAVE massive MIMO

Journal Article (2019)
Author(s)

Yu Zhang (Nanjing University of Aeronautics and Astronautics, George Mason University)

Yue Wang (Nanjing University of Aeronautics and Astronautics)

Zhi Tian (Nanjing University of Aeronautics and Astronautics)

Geert Leus (TU Delft - Signal Processing Systems)

Gong Zhang (George Mason University)

Research Group
Signal Processing Systems
DOI related publication
https://doi.org/10.1109/GLOBECOM38437.2019.9014327
More Info
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Publication Year
2019
Language
English
Research Group
Signal Processing Systems

Abstract

This paper develops efficient super-resolution spatial channel covariance estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under the hybrid precoding constraint. Two structure-based optimization techniques including low-rank structured covariance reconstruction and dynamic atomic norm minimization are proposed to accurately estimate the channel covariance matrix. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. The extension of this work to the higher-dimensional cases is also discussed. Simulation results verify the effectiveness of the proposed methods in hybrid mmWave massive MIMO systems.

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