Monotonic optimization based decoding for linear codes

Tuan, HD; Son, TT; Tuy, H; Khoa, PT

Monotonic optimization based decoding for linear codes

Tuan, HD

Son, TT Tuy, H Khoa, PT

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Publication Type:: Journal Article
Citation:: Journal of Global Optimization, 2013, 55 (2), pp. 301 - 312
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Son, TT	en_US
dc.contributor.author	Tuy, H	en_US
dc.contributor.author	Khoa, PT	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Journal of Global Optimization, 2013, 55 (2), pp. 301 - 312	en_US
dc.identifier.issn	0925-5001	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26434
dc.description.abstract	New efficient methods are developed for the optimal maximum-likelihood (ML) decoding of an arbitrary binary linear code based on data received from any discrete Gaussian channel. The decoding algorithm is based on monotonic optimization that is minimizing a difference of monotonic (d.m.) objective functions subject to the 0-1 constraints of bit variables. The iterative process converges to the global optimal ML solution after finitely many steps. The proposed algorithm's computational complexity depends on input sequence length k which is much less than the codeword length n, especially for a codes with small code rate. The viability of the developed is verified through simulations on different coding schemes. © 2011 Springer Science+Business Media, LLC.	en_US
dc.relation.ispartof	Journal of Global Optimization	en_US
dc.relation.isbasedon	10.1007/s10898-011-9816-9	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Monotonic optimization based decoding for linear codes	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	55	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	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
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	55	en_US

Abstract:

New efficient methods are developed for the optimal maximum-likelihood (ML) decoding of an arbitrary binary linear code based on data received from any discrete Gaussian channel. The decoding algorithm is based on monotonic optimization that is minimizing a difference of monotonic (d.m.) objective functions subject to the 0-1 constraints of bit variables. The iterative process converges to the global optimal ML solution after finitely many steps. The proposed algorithm's computational complexity depends on input sequence length k which is much less than the codeword length n, especially for a codes with small code rate. The viability of the developed is verified through simulations on different coding schemes. © 2011 Springer Science+Business Media, LLC.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26434