Rank metric convolutional codes for random linear network coding

Antonia Wachter-Zeh, Vladimir Sidorenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Random Linear Network Coding (RLNC) currently attracts a lot of attention as a technique to disseminate information in a network. In this contribution, non-coherent multi-shot RLNC is considered, that means, the unknown and time variant network is used several times. In order to create dependencies between the different shots, convolutional network codes are used, in particular Partial Unit Memory (PUM) codes. Such PUM codes based on rank metric block codes are constructed and it is shown how they can efficiently be decoded when errors, erasures and deviations occur. The decoding complexity of this algorithm is cubic with the length. Further, it is described how lifting of these codes can be applied for error correction in RLNC.

Original languageEnglish
Title of host publication2012 International Symposium on Network Coding, NetCod 2012
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Print)9781467318921
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 International Symposium on Network Coding, NetCod 2012 - Cambridge, MA, United States
Duration: 29 Jun 201230 Jun 2012

Publication series

Name2012 International Symposium on Network Coding, NetCod 2012

Conference

Conference2012 International Symposium on Network Coding, NetCod 2012
Country/TerritoryUnited States
CityCambridge, MA
Period29/06/1230/06/12

Keywords

  • Convolutional codes
  • Gabidulin codes
  • Network coding
  • Partial unit memory codes
  • Rank metric

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