Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity

Zhang, JA; Chen, Z; Cheng, P; Huang, X

Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity

Zhang, JA

Chen, Z Cheng, P Huang, X

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Publication Type:: Journal Article
Citation:: Signal Processing, 2016, 118 pp. 153 - 158
Issue Date:: 2016-07-25

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Field	Value	Language
dc.contributor.author	Zhang, JA https://orcid.org/0000-0002-6102-3762	en_US
dc.contributor.author	Chen, Z	en_US
dc.contributor.author	Cheng, P	en_US
dc.contributor.author	Huang, X https://orcid.org/0000-0001-6869-5732	en_US
dc.date.issued	2016-07-25	en_US
dc.identifier.citation	Signal Processing, 2016, 118 pp. 153 - 158	en_US
dc.identifier.issn	0165-1684	en_US
dc.identifier.uri	http://hdl.handle.net/10453/52286
dc.description.abstract	© 2015 Elsevier B.V. Abstract Sparse Bayesian learning (SBL) has high computational complexity associated with matrix inversion in each iteration. In this paper, we investigate complexity reduced multiple-measurement vector (MMV) based implementation for single-measurement vector SBL problems. For problems with special structured sensing matrices, we propose two sub-optimal SBL schemes with significantly reduced complexity and slight estimation performance degradation, by exploiting the deterministic correlation in the converted MMV model explicitly. Two application scenarios on channel estimation in multicarrier systems and direction of arrival estimation are presented. Simulation results validate the effectiveness of the schemes.	en_US
dc.relation.ispartof	Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.sigpro.2015.06.020	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity	en_US
dc.type	Journal Article
utslib.citation.volume	118	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	118	en_US

Abstract:

© 2015 Elsevier B.V. Abstract Sparse Bayesian learning (SBL) has high computational complexity associated with matrix inversion in each iteration. In this paper, we investigate complexity reduced multiple-measurement vector (MMV) based implementation for single-measurement vector SBL problems. For problems with special structured sensing matrices, we propose two sub-optimal SBL schemes with significantly reduced complexity and slight estimation performance degradation, by exploiting the deterministic correlation in the converted MMV model explicitly. Two application scenarios on channel estimation in multicarrier systems and direction of arrival estimation are presented. Simulation results validate the effectiveness of the schemes.

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http://hdl.handle.net/10453/52286