Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

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. H...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 19; číslo 15; s. 3368
Hlavní autoři:	Hu, Rui, Tong, Jun, Xi, Jiangtao, Guo, Qinghua, Yu, Yanguang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 31.07.2019 MDPI
Témata:	Arrays channel covariance estimation Communication Decomposition Design Dictionaries Economic models Estimating techniques hybrid MIMO Information processing millimeter wave communications Transmitters Canada United States > US millimeter wave communications hybrid MIMO channel covariance estimation
ISSN:	1424-8220, 1424-8220
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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.
AbstractList	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. 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.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.
Author	Tong, Jun Guo, Qinghua Xi, Jiangtao Hu, Rui Yu, Yanguang
AuthorAffiliation	School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
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Cites_doi	10.1109/TVT.2012.2185076 10.1109/JSAC.2017.2699338 10.1109/TSP.2016.2616336 10.1016/S0377-0427(00)00393-9 10.1109/TWC.2018.2873592 10.1109/ACCESS.2018.2877432 10.1109/COMST.2018.2844322 10.1007/978-3-319-24486-0_1 10.1109/CAMSAP.2013.6714008 10.1109/JSTSP.2016.2523903 10.1109/LGRS.2018.2799329 10.1109/TWC.2014.011714.130846 10.1109/ACCESS.2015.2514261 10.1007/s11276-015-0942-z 10.1109/TCOMM.2016.2557791 10.1109/TSP.2013.2279355 10.1109/JSAC.2014.2328154 10.1109/JSTSP.2014.2334278 10.1109/GLOCOM.2018.8647176 10.1109/TIT.2013.2269476 10.1109/EUSIPCO.2016.7760261 10.1109/TWC.2018.2808523 10.1109/ACSSC.2016.7869611 10.1109/GlobalSIP.2018.8646605 10.1109/TWC.2018.2821667
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SubjectTerms	Arrays channel covariance estimation Communication Decomposition Design Dictionaries Economic models Estimating techniques hybrid MIMO Information processing millimeter wave communications Transmitters
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