Channel covariance matrix estimation via dimension reduction for hybrid MIMO Mmwave communication systems

Authors

Rui Hu, University of WollongongFollow
Jun Tong, University of WollongongFollow
Jiangtao Xi, University of WollongongFollow
Qinghua Guo, University of WollongongFollow
Yanguang Yu, University of WollongongFollow

RIS ID

138115

Publication Details

R. Hu, J. Tong, J. Xi, Q. Guo & Y. Yu, "Channel covariance matrix estimation via dimension reduction for hybrid MIMO Mmwave communication systems," Sensors (Switzerland), vol. 19, (15) pp. 3368-1-3368-20, 2019.

Abstract

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as potential candidates for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method.