A maximum likelihood decoding algorithm of Gabidulin codes in deterministic network coding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, studies of linear network coding have attracted attention. This paper considers a probabilistic error model for the deterministic linear network coding. In the previous studies of error-correcting codes, Kaneko et al. proposed a decoding algorithms for probabilistic error model. This is a maximum likelihood decoding algorithm that uses the optimization method called branch and bound method. This paper constructs a new model of deterministic linear network coding and proposes a maximum likelihood decoding algorithm that uses the branch and bound method.

Original languageEnglish
Title of host publicationProceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages666-670
Number of pages5
ISBN (Electronic)9784885523090
Publication statusPublished - 2017 Feb 2
Event3rd International Symposium on Information Theory and Its Applications, ISITA 2016 - Monterey, United States
Duration: 2016 Oct 302016 Nov 2

Other

Other3rd International Symposium on Information Theory and Its Applications, ISITA 2016
CountryUnited States
CityMonterey
Period16/10/3016/11/2

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Library and Information Sciences

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  • Cite this

    Kazama, K., Kamatsuka, A., & Matsushima, T. (2017). A maximum likelihood decoding algorithm of Gabidulin codes in deterministic network coding. In Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016 (pp. 666-670). [7840508] Institute of Electrical and Electronics Engineers Inc..