Superregular matrices and applications to convolutional codes

Bibliographic Details
Main Author: Almeida, P. J.
Publication Date: 2016
Other Authors: Napp, D., Pinto, R.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10773/15837
Summary: The main results of this paper are twofold: the first one is a matrix theoretical result. We say that a matrix is superregular if all of its minors that are not trivially zero are nonzero. Given a a×b, a ≥ b, superregular matrix over a field, we show that if all of its rows are nonzero then any linear combination of its columns, with nonzero coefficients, has at least a−b + 1 nonzero entries. Secondly, we make use of this result to construct convolutional codes that attain the maximum possible distance for some fixed parameters of the code, namely, the rate and the Forney indices. These results answer some open questions on distances and constructions of convolutional codes posted in the literature.
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spelling Superregular matrices and applications to convolutional codesConvolutional codeForney indicesOptimal codeSuperregular matrixThe main results of this paper are twofold: the first one is a matrix theoretical result. We say that a matrix is superregular if all of its minors that are not trivially zero are nonzero. Given a a×b, a ≥ b, superregular matrix over a field, we show that if all of its rows are nonzero then any linear combination of its columns, with nonzero coefficients, has at least a−b + 1 nonzero entries. Secondly, we make use of this result to construct convolutional codes that attain the maximum possible distance for some fixed parameters of the code, namely, the rate and the Forney indices. These results answer some open questions on distances and constructions of convolutional codes posted in the literature.Elsevier2016-06-29T11:16:59Z2016-06-15T00:00:00Z2016-06-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/15837eng0024-379510.1016/j.laa.2016.02.034Almeida, P. J.Napp, D.Pinto, R.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-06T03:57:08Zoai:ria.ua.pt:10773/15837Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T13:52:17.809317Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Superregular matrices and applications to convolutional codes
title Superregular matrices and applications to convolutional codes
spellingShingle Superregular matrices and applications to convolutional codes
Almeida, P. J.
Convolutional code
Forney indices
Optimal code
Superregular matrix
title_short Superregular matrices and applications to convolutional codes
title_full Superregular matrices and applications to convolutional codes
title_fullStr Superregular matrices and applications to convolutional codes
title_full_unstemmed Superregular matrices and applications to convolutional codes
title_sort Superregular matrices and applications to convolutional codes
author Almeida, P. J.
author_facet Almeida, P. J.
Napp, D.
Pinto, R.
author_role author
author2 Napp, D.
Pinto, R.
author2_role author
author
dc.contributor.author.fl_str_mv Almeida, P. J.
Napp, D.
Pinto, R.
dc.subject.por.fl_str_mv Convolutional code
Forney indices
Optimal code
Superregular matrix
topic Convolutional code
Forney indices
Optimal code
Superregular matrix
description The main results of this paper are twofold: the first one is a matrix theoretical result. We say that a matrix is superregular if all of its minors that are not trivially zero are nonzero. Given a a×b, a ≥ b, superregular matrix over a field, we show that if all of its rows are nonzero then any linear combination of its columns, with nonzero coefficients, has at least a−b + 1 nonzero entries. Secondly, we make use of this result to construct convolutional codes that attain the maximum possible distance for some fixed parameters of the code, namely, the rate and the Forney indices. These results answer some open questions on distances and constructions of convolutional codes posted in the literature.
publishDate 2016
dc.date.none.fl_str_mv 2016-06-29T11:16:59Z
2016-06-15T00:00:00Z
2016-06-15
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url http://hdl.handle.net/10773/15837
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0024-3795
10.1016/j.laa.2016.02.034
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