Distributed bayesian compressive sensing based blind carrier-frequency offset estimation for interleaved OFDMA uplink

Cheng, P; Chen, Z; Guo, YJ; Gui, L

Distributed bayesian compressive sensing based blind carrier-frequency offset estimation for interleaved OFDMA uplink

Cheng, P Chen, Z Guo, YJ

Gui, L

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Publication Type:: Conference Proceeding
Citation:: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2013, pp. 801 - 806
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Cheng, P	en_US
dc.contributor.author	Chen, Z	en_US
dc.contributor.author	Guo, YJ https://orcid.org/0000-0001-6008-9682	en_US
dc.contributor.author	Gui, L	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2013, pp. 801 - 806	en_US
dc.identifier.isbn	9781467362351	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120150
dc.description.abstract	Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering the received signal structure, the new method first constructs a measurement matrix associated with a sparse signal matrix weight, which sets up the stage for the application of CS theory in tackling the original estimation problem. Then, the DBCS theory that exploits a common sparse profile of the sparse signal matrix weight is employed to distributively estimate a sparse hyperparameter vector, whose significant peaks are linked to the correct estimation of the multiple CFOs. Compared with the existing subspace theory based methods, the proposed scheme offers a significant enhancement in estimation accuracy, in specific in the low signal-to-noise ratio (SNR) region. The numerical results validate the effectiveness of the proposed scheme. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC	en_US
dc.relation.isbasedon	10.1109/PIMRC.2013.6666246	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Distributed bayesian compressive sensing based blind carrier-frequency offset estimation for interleaved OFDMA uplink	en_US
dc.type	Conference Proceeding
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

Abstract:

Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering the received signal structure, the new method first constructs a measurement matrix associated with a sparse signal matrix weight, which sets up the stage for the application of CS theory in tackling the original estimation problem. Then, the DBCS theory that exploits a common sparse profile of the sparse signal matrix weight is employed to distributively estimate a sparse hyperparameter vector, whose significant peaks are linked to the correct estimation of the multiple CFOs. Compared with the existing subspace theory based methods, the proposed scheme offers a significant enhancement in estimation accuracy, in specific in the low signal-to-noise ratio (SNR) region. The numerical results validate the effectiveness of the proposed scheme. © 2013 IEEE.

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