Joint source-channel optimization of vector quantization with polar codes

Mohammadi, MS; Dutkiewicz, E; Zhang, Q

Joint source-channel optimization of vector quantization with polar codes

Mohammadi, MS Dutkiewicz, E

Zhang, Q

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Publication Type:: Conference Proceeding
Citation:: IEEE Vehicular Technology Conference, 2016, 0
Issue Date:: 2016-07-02

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Field	Value	Language
dc.contributor.author	Mohammadi, MS	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Zhang, Q	en_US
dc.date.issued	2016-07-02	en_US
dc.identifier.citation	IEEE Vehicular Technology Conference, 2016, 0	en_US
dc.identifier.isbn	9781509017010	en_US
dc.identifier.issn	1550-2252	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126002
dc.description.abstract	© 2016 IEEE. Joint application of polar channel coding combined with vector quantization lossy source coding is considered in this paper. The existing index assignment schemes in the literature cannot be used with polar codes due to their unique crossover probabilities. We elaborate on this problem and locally optimize index assignments. In addition, we propose an algorithm that jointly optimizes the number of quantization levels and the rate of the polar code in order to achieve minimum end-to-end distortion. It finds the optimal tradeoff between the distortion caused by channel errors and the quantization distortion. We also derive estimates for the crossover probabilities of the polar code which are required in the analysis. Simulation results confirm the effectiveness of the proposed algorithms and the accuracy of the crossover probabilities.	en_US
dc.relation.ispartof	IEEE Vehicular Technology Conference	en_US
dc.relation.isbasedon	10.1109/VTCFall.2016.7881061	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Joint source-channel optimization of vector quantization with polar codes	en_US
dc.type	Conference Proceeding
utslib.citation.volume	0	en_US
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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	0	en_US

Abstract:

© 2016 IEEE. Joint application of polar channel coding combined with vector quantization lossy source coding is considered in this paper. The existing index assignment schemes in the literature cannot be used with polar codes due to their unique crossover probabilities. We elaborate on this problem and locally optimize index assignments. In addition, we propose an algorithm that jointly optimizes the number of quantization levels and the rate of the polar code in order to achieve minimum end-to-end distortion. It finds the optimal tradeoff between the distortion caused by channel errors and the quantization distortion. We also derive estimates for the crossover probabilities of the polar code which are required in the analysis. Simulation results confirm the effectiveness of the proposed algorithms and the accuracy of the crossover probabilities.

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http://hdl.handle.net/10453/126002