Superregular matrices and applications to convolutional codes
| Main Author: | |
|---|---|
| Publication Date: | 2016 |
| Other Authors: | , |
| 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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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 |
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2016-06-29T11:16:59Z 2016-06-15T00:00:00Z 2016-06-15 |
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info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
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article |
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publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/15837 |
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http://hdl.handle.net/10773/15837 |
| dc.language.iso.fl_str_mv |
eng |
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eng |
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0024-3795 10.1016/j.laa.2016.02.034 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Elsevier |
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Elsevier |
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