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

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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Bibliographic Details
Main Authors: Rui Hu, Jun Tong, Jiangtao Xi, Qinghua Guo, Yanguang Yu
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Sensors
Subjects:
millimeter wave communications
hybrid MIMO
channel covariance estimation
Online Access:https://www.mdpi.com/1424-8220/19/15/3368
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spelling doaj-dba168dc79684ac89969f023c246a43a2020-11-25T01:17:07ZengMDPI AGSensors1424-82202019-07-011915336810.3390/s19153368s19153368Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication SystemsRui Hu0Jun Tong1Jiangtao Xi2Qinghua Guo3Yanguang Yu4School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaHybrid 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.https://www.mdpi.com/1424-8220/19/15/3368millimeter wave communicationshybrid MIMOchannel covariance estimation
collection DOAJ
language English
format Article
sources DOAJ
author Rui Hu
Jun Tong
Jiangtao Xi
Qinghua Guo
Yanguang Yu
spellingShingle Rui Hu
Jun Tong
Jiangtao Xi
Qinghua Guo
Yanguang Yu
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Sensors
millimeter wave communications
hybrid MIMO
channel covariance estimation
author_facet Rui Hu
Jun Tong
Jiangtao Xi
Qinghua Guo
Yanguang Yu
author_sort Rui Hu
title Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
title_short Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
title_full Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
title_fullStr Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
title_full_unstemmed Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
title_sort channel covariance matrix estimation via dimension reduction for hybrid mimo mmwave communication systems
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2019-07-01
description 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.
topic millimeter wave communications
hybrid MIMO
channel covariance estimation
url https://www.mdpi.com/1424-8220/19/15/3368
work_keys_str_mv AT ruihu channelcovariancematrixestimationviadimensionreductionforhybridmimommwavecommunicationsystems
AT juntong channelcovariancematrixestimationviadimensionreductionforhybridmimommwavecommunicationsystems
AT jiangtaoxi channelcovariancematrixestimationviadimensionreductionforhybridmimommwavecommunicationsystems
AT qinghuaguo channelcovariancematrixestimationviadimensionreductionforhybridmimommwavecommunicationsystems
AT yanguangyu channelcovariancematrixestimationviadimensionreductionforhybridmimommwavecommunicationsystems
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