Near-ML Low-Complexity Detection for Generalized Spatial Modulation

Wang, C; Cheng, P; Chen, Z; Zhang, JA; Xiao, Y; Gui, L

Near-ML Low-Complexity Detection for Generalized Spatial Modulation

Wang, C Cheng, P Chen, Z Zhang, JA

Xiao, Y Gui, L

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Publication Type:: Journal Article
Citation:: IEEE Communications Letters, 2016, 20 (3), pp. 618 - 621
Issue Date:: 2016-03-01

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Field	Value	Language
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Cheng, P	en_US
dc.contributor.author	Chen, Z	en_US
dc.contributor.author	Zhang, JA https://orcid.org/0000-0002-6102-3762	en_US
dc.contributor.author	Xiao, Y	en_US
dc.contributor.author	Gui, L	en_US
dc.date.issued	2016-03-01	en_US
dc.identifier.citation	IEEE Communications Letters, 2016, 20 (3), pp. 618 - 621	en_US
dc.identifier.issn	1089-7798	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122793
dc.description.abstract	© 2016 IEEE. Generalized spatial modulation (GSM) is a spectral and energy efficient multiple-input-multiple-output transmission technique. The low-complexity detection algorithm design with near maximum likelihood (ML) performance at the receiver is very challenging, and is the focus of this letter. In specific, we exploit the fixed sparsity constraint in the transmitted GSM signals, and take advantage of Bayesian compressive sensing (BCS) in sparse signal recovery. A new detection algorithm, referred to as enhanced Bayesian compressive sensing (EBCS), is proposed. It features more than 75% complexity reduction at high signal-to-noise ratios compared with the ordered-blocked minimum-mean-squared-error algorithm. Furthermore, it is shown by simulation that its error performance is comparable to the ML algorithm, and the performance gap is negligible in many cases.	en_US
dc.relation.ispartof	IEEE Communications Letters	en_US
dc.relation.isbasedon	10.1109/LCOMM.2016.2516542	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Near-ML Low-Complexity Detection for Generalized Spatial Modulation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	20	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
utslib.copyright.status	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 2016 IEEE. Generalized spatial modulation (GSM) is a spectral and energy efficient multiple-input-multiple-output transmission technique. The low-complexity detection algorithm design with near maximum likelihood (ML) performance at the receiver is very challenging, and is the focus of this letter. In specific, we exploit the fixed sparsity constraint in the transmitted GSM signals, and take advantage of Bayesian compressive sensing (BCS) in sparse signal recovery. A new detection algorithm, referred to as enhanced Bayesian compressive sensing (EBCS), is proposed. It features more than 75% complexity reduction at high signal-to-noise ratios compared with the ordered-blocked minimum-mean-squared-error algorithm. Furthermore, it is shown by simulation that its error performance is comparable to the ML algorithm, and the performance gap is negligible in many cases.

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http://hdl.handle.net/10453/122793