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

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posted on 2024-11-15, 15:53 authored by Rui Hu, Jun TongJun Tong, Jiangtao XiJiangtao Xi, Qinghua GuoQinghua Guo, Yanguang YuYanguang Yu
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.

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Citation

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.

Journal title

Sensors (Switzerland)

Volume

19

Issue

15

Language

English

RIS ID

138115

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