Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems

Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems

Wang, Yue (George Mason University)
Zhang, Yu (Nanjing University of Aeronautics and Astronautics; George Mason University)
Tian, Zhi (George Mason University)
Leus, G.J.T. (TU Delft Signal Processing Systems)
Zhang, Gong (Nanjing University of Aeronautics and Astronautics)

2019

This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. The different time-varying scales of channel path angles versus path gains are capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spurious-peak cancellation in non-uniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays.

Arbitrary array
gridless compressive sensing
hybrid structure
Jacobi-Anger approximation
mmWave massive MIMO
super-resolution channel estimation
Vandermonde structure

http://resolver.tudelft.nl/uuid:86521684-e047-449a-98c2-335de1de6fa9

https://doi.org/10.1109/JSTSP.2019.2937632

2020-02-26

1932-4553

IEEE Journal of Selected Topics in Signal Processing, 13 (5), 947-960

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2019 Yue Wang, Yu Zhang, Zhi Tian, G.J.T. Leus, Gong Zhang