Maximum rank distance profile codes
| Main Author: | |
|---|---|
| Publication Date: | 2022 |
| Other Authors: | |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10773/36225 |
Summary: | Network coding theory provides a pragmatic instrument to disseminate information (packets) over networks where there may be many information sources and possibly many receivers. From a mathematical point of view, these packets can be modelled by columns of matrices over a finite field Fq and during the transmission, these columns are linearly combined at each node of the network. To achieve reliable communication over this channel, rank-metric codes are typically employed. Nevertheless, network coding techniques for streaming are fundamentally different from the classical ones. To be optimised they must operate under low-latency, sequential encoding and decoding constraints, and as such they must inherently have a convolutional structure. That is the reason why most of the proposed schemes for this scenario employ convolutional codes in different ways. |
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Maximum rank distance profile codesRank-metric codesOptimalRank-deficiency channelDecoding methodNetwork coding theory provides a pragmatic instrument to disseminate information (packets) over networks where there may be many information sources and possibly many receivers. From a mathematical point of view, these packets can be modelled by columns of matrices over a finite field Fq and during the transmission, these columns are linearly combined at each node of the network. To achieve reliable communication over this channel, rank-metric codes are typically employed. Nevertheless, network coding techniques for streaming are fundamentally different from the classical ones. To be optimised they must operate under low-latency, sequential encoding and decoding constraints, and as such they must inherently have a convolutional structure. That is the reason why most of the proposed schemes for this scenario employ convolutional codes in different ways.2023-02-03T19:42:53Z2022-10-07T00:00:00Z2022-10-07conference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10773/36225engPinto, RaquelVela, Carlosinfo: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:RCAAP2024-05-06T04:40:55Zoai:ria.ua.pt:10773/36225Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:16:46.644255Repositórios 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 |
Maximum rank distance profile codes |
| title |
Maximum rank distance profile codes |
| spellingShingle |
Maximum rank distance profile codes Pinto, Raquel Rank-metric codes Optimal Rank-deficiency channel Decoding method |
| title_short |
Maximum rank distance profile codes |
| title_full |
Maximum rank distance profile codes |
| title_fullStr |
Maximum rank distance profile codes |
| title_full_unstemmed |
Maximum rank distance profile codes |
| title_sort |
Maximum rank distance profile codes |
| author |
Pinto, Raquel |
| author_facet |
Pinto, Raquel Vela, Carlos |
| author_role |
author |
| author2 |
Vela, Carlos |
| author2_role |
author |
| dc.contributor.author.fl_str_mv |
Pinto, Raquel Vela, Carlos |
| dc.subject.por.fl_str_mv |
Rank-metric codes Optimal Rank-deficiency channel Decoding method |
| topic |
Rank-metric codes Optimal Rank-deficiency channel Decoding method |
| description |
Network coding theory provides a pragmatic instrument to disseminate information (packets) over networks where there may be many information sources and possibly many receivers. From a mathematical point of view, these packets can be modelled by columns of matrices over a finite field Fq and during the transmission, these columns are linearly combined at each node of the network. To achieve reliable communication over this channel, rank-metric codes are typically employed. Nevertheless, network coding techniques for streaming are fundamentally different from the classical ones. To be optimised they must operate under low-latency, sequential encoding and decoding constraints, and as such they must inherently have a convolutional structure. That is the reason why most of the proposed schemes for this scenario employ convolutional codes in different ways. |
| publishDate |
2022 |
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2022-10-07T00:00:00Z 2022-10-07 2023-02-03T19:42:53Z |
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conference object |
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info:eu-repo/semantics/publishedVersion |
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publishedVersion |
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http://hdl.handle.net/10773/36225 |
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http://hdl.handle.net/10773/36225 |
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eng |
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eng |
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openAccess |
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application/pdf |
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