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Skew Convolutional Codes
A new class of convolutional codes, called skew convolutional codes, that extends the class of classical fixed convolutional codes, is proposed. Skew convolutional codes can be represented as periodic time-varying convolutional codes but have a description as compact as fixed convolutional codes. De...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761554/ https://www.ncbi.nlm.nih.gov/pubmed/33276694 http://dx.doi.org/10.3390/e22121364 |
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author | Sidorenko, Vladimir Li, Wenhui Günlü, Onur Kramer, Gerhard |
author_facet | Sidorenko, Vladimir Li, Wenhui Günlü, Onur Kramer, Gerhard |
author_sort | Sidorenko, Vladimir |
collection | PubMed |
description | A new class of convolutional codes, called skew convolutional codes, that extends the class of classical fixed convolutional codes, is proposed. Skew convolutional codes can be represented as periodic time-varying convolutional codes but have a description as compact as fixed convolutional codes. Designs of generator and parity check matrices, encoders, and code trellises for skew convolutional codes and their duals are shown. For memoryless channels, one can apply Viterbi or BCJR decoding algorithms, or a dualized BCJR algorithm, to decode skew convolutional codes. |
format | Online Article Text |
id | pubmed-7761554 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77615542021-02-24 Skew Convolutional Codes Sidorenko, Vladimir Li, Wenhui Günlü, Onur Kramer, Gerhard Entropy (Basel) Article A new class of convolutional codes, called skew convolutional codes, that extends the class of classical fixed convolutional codes, is proposed. Skew convolutional codes can be represented as periodic time-varying convolutional codes but have a description as compact as fixed convolutional codes. Designs of generator and parity check matrices, encoders, and code trellises for skew convolutional codes and their duals are shown. For memoryless channels, one can apply Viterbi or BCJR decoding algorithms, or a dualized BCJR algorithm, to decode skew convolutional codes. MDPI 2020-12-02 /pmc/articles/PMC7761554/ /pubmed/33276694 http://dx.doi.org/10.3390/e22121364 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sidorenko, Vladimir Li, Wenhui Günlü, Onur Kramer, Gerhard Skew Convolutional Codes |
title | Skew Convolutional Codes |
title_full | Skew Convolutional Codes |
title_fullStr | Skew Convolutional Codes |
title_full_unstemmed | Skew Convolutional Codes |
title_short | Skew Convolutional Codes |
title_sort | skew convolutional codes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761554/ https://www.ncbi.nlm.nih.gov/pubmed/33276694 http://dx.doi.org/10.3390/e22121364 |
work_keys_str_mv | AT sidorenkovladimir skewconvolutionalcodes AT liwenhui skewconvolutionalcodes AT gunluonur skewconvolutionalcodes AT kramergerhard skewconvolutionalcodes |