Superregular matrices and applications to convolutional codes

Detalhes bibliográficos
Autor(a) principal: Almeida, P. J.
Data de Publicação: 2016
Outros Autores: Napp, D., Pinto, R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/15837
Resumo: 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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:29:03Zoai:ria.ua.pt:10773/15837Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:51:00.137917Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
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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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/15837
url http://hdl.handle.net/10773/15837
dc.language.iso.fl_str_mv eng
language eng
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10.1016/j.laa.2016.02.034
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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