Next Article in Journal
Numerical Analysis with Keller-Box Scheme for Stagnation Point Effect on Flow of Micropolar Nanofluid over an Inclined Surface
Previous Article in Journal
Integrability Properties of Cubic Liénard Oscillators with Linear Damping
Open AccessArticle

On Matrix Completion-Based Channel Estimators for Massive MIMO Systems

1
Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin Polytechnic University, Tianjin 300387, China
2
School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
3
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
*
Authors to whom correspondence should be addressed.
Symmetry 2019, 11(11), 1377; https://doi.org/10.3390/sym11111377
Received: 19 October 2019 / Revised: 4 November 2019 / Accepted: 4 November 2019 / Published: 6 November 2019
Large-scale symmetric arrays such as uniform linear arrays (ULA) have been widely used in wireless communications for improving spectrum efficiency and reliability. Channel state information (CSI) is critical for optimizing massive multiple-input multiple-output(MIMO)-based wireless communication systems. The acquisition of CSI for massive MIMO faces challenges such as training shortage and high computational complexity. For millimeter wave MIMO systems, the low-rankness of the channel can be utilized to address the challenge of training shortage. In this paper, we compared several channel estimation schemes based on matrix completion (MC) for symmetrical arrays. Performance and computational complexity are discussed and compared. By comparing the performance in different scenarios, we concluded that the generalized conditional gradient with alternating minimization (GCG-Alt) estimator provided a low-cost, robust solution, while the alternating direction method of multipliers (ADMM)-based hybrid methods achieved the best performance when the array response was perfectly known.
Keywords: low-rankness; massive MIMO; matrix completion; compressive sensing low-rankness; massive MIMO; matrix completion; compressive sensing
Show Figures

Graphical abstract

MDPI and ACS Style

Ding , M.; Yang , X.; Hu , R.; Xiao , Z.; Tong , J.; Xi , J. On Matrix Completion-Based Channel Estimators for Massive MIMO Systems. Symmetry 2019, 11, 1377.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop