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

Journal Article


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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.

Publication Date


  • 2019

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.

Scopus Eid


  • 2-s2.0-85070517879

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4112&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3093

Start Page


  • 3368-1

End Page


  • 3368-20

Volume


  • 19

Issue


  • 15

Place Of Publication


  • Switzerland

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.

Publication Date


  • 2019

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.

Scopus Eid


  • 2-s2.0-85070517879

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4112&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3093

Start Page


  • 3368-1

End Page


  • 3368-20

Volume


  • 19

Issue


  • 15

Place Of Publication


  • Switzerland