Sparse Channel Modelling Using Multi-Measurement Vector Compressive Sensing

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

Sparse Channel Modelling Using Multi-Measurement Vector Compressive Sensing

Cui, PF

Zhang, JA

Lu, WJ Guo, YJ

Zhu, HB

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Publication Type:: Conference Proceeding
Citation:: 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018
Issue Date:: 2018-01-01

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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, HB	en_US
dc.date.available	2021-03-22T18:03:41Z
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018	en_US
dc.identifier.isbn	9781538647271	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131745
dc.description.abstract	© 2018 IEEE. Channel sparsity is well exploited for channel estimation, but there is very limited work on sparse channel modelling, which studies and characterizes the statistical properties of sparse channel coefficients. In this paper, we study sparse channel modelling using real measured channel data in off-body signal propagation. We propose multi-measurement vector based compressive sensing algorithms for extracting sparse channel coefficients, study the statistical properties of these extracted coefficients, and develop an algorithm for generating simulated channels using the statistical sparse model. The proposed method can be directly applied to other channel measurements, and is very useful for channel simulation and developing advanced sparse channel estimation schemes.	en_US
dc.relation.ispartof	2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings	en_US
dc.relation.isbasedon	10.1109/GLOCOM.2018.8648115	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Sparse Channel Modelling Using Multi-Measurement Vector Compressive Sensing	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	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	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. Channel sparsity is well exploited for channel estimation, but there is very limited work on sparse channel modelling, which studies and characterizes the statistical properties of sparse channel coefficients. In this paper, we study sparse channel modelling using real measured channel data in off-body signal propagation. We propose multi-measurement vector based compressive sensing algorithms for extracting sparse channel coefficients, study the statistical properties of these extracted coefficients, and develop an algorithm for generating simulated channels using the statistical sparse model. The proposed method can be directly applied to other channel measurements, and is very useful for channel simulation and developing advanced sparse channel estimation schemes.

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http://hdl.handle.net/10453/131745