Statistical Sparse Channel Modeling for Measured and Simulated Wireless Temporal Channels

Cui, PF; Zhang, JA; Lu, WJ; Guo, YJ; Zhu, H

Statistical Sparse Channel Modeling for Measured and Simulated Wireless Temporal Channels

Cui, PF

Zhang, JA

Lu, WJ Guo, YJ

Zhu, H

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2019, 18 (12), pp. 5868 - 5881
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Cui, PF https://orcid.org/0000-0001-8594-7460	en_US
dc.contributor.author	Zhang, JA https://orcid.org/0000-0002-6102-3762	en_US
dc.contributor.author	Lu, WJ	en_US
dc.contributor.author	Guo, YJ https://orcid.org/0000-0001-6008-9682	en_US
dc.contributor.author	Zhu, H	en_US
dc.date.accessioned	2020-04-02T05:42:10Z
dc.date.available	2020-04-02T05:42:10Z
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2019, 18 (12), pp. 5868 - 5881	en_US
dc.identifier.issn	1536-1276	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139751
dc.description.abstract	© 2002-2012 IEEE. Time-domain wireless channels are generally modeled by Tapped Delay Line (TDL) model and its variants. These models are not effective for channel representation and estimation when the number of multipath taps is large. Compressive sensing (CS) provides a powerful tool for sparse channel modeling and estimation. Most of the research has been focusing on sparse channel estimation, while sparse channel modeling (SCM) is rarely considered for centimetre-wave channels. In this paper, we investigate statistical sparse channel modeling, using both measured and simulated channels over a frequency range of 6 to 8.5 GHz. We first introduce the triple equilibrium principle to explore the trade-off between sparsity, modeling accuracy, and algorithm complexity in SCM, and provide a methodology for characterizing the sparsity of time-domain channels using single-measurement-vector compressive sensing algorithms. Using mainly the selected wavelet dictionary and various CS reconstruction (aka recovery) algorithms, we then present comprehensive statistical sparse channel models, including channel sparsity, magnitude decaying profile, sparse coefficient distribution and atomic index distribution. Connections between the parameters of conventional TDL and sparse channel models are mathematically established. We also propose three methods for generating simulated channels from the developed sparse channel models, which validates their effectiveness.	en_US
dc.relation.ispartof	IEEE Transactions on Wireless Communications	en_US
dc.relation.isbasedon	10.1109/TWC.2019.2940017	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Statistical Sparse Channel Modeling for Measured and Simulated Wireless Temporal Channels	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	18	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

© 2002-2012 IEEE. Time-domain wireless channels are generally modeled by Tapped Delay Line (TDL) model and its variants. These models are not effective for channel representation and estimation when the number of multipath taps is large. Compressive sensing (CS) provides a powerful tool for sparse channel modeling and estimation. Most of the research has been focusing on sparse channel estimation, while sparse channel modeling (SCM) is rarely considered for centimetre-wave channels. In this paper, we investigate statistical sparse channel modeling, using both measured and simulated channels over a frequency range of 6 to 8.5 GHz. We first introduce the triple equilibrium principle to explore the trade-off between sparsity, modeling accuracy, and algorithm complexity in SCM, and provide a methodology for characterizing the sparsity of time-domain channels using single-measurement-vector compressive sensing algorithms. Using mainly the selected wavelet dictionary and various CS reconstruction (aka recovery) algorithms, we then present comprehensive statistical sparse channel models, including channel sparsity, magnitude decaying profile, sparse coefficient distribution and atomic index distribution. Connections between the parameters of conventional TDL and sparse channel models are mathematically established. We also propose three methods for generating simulated channels from the developed sparse channel models, which validates their effectiveness.

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http://hdl.handle.net/10453/139751