Multidimensional decoding networks for trapping set analysis

Allison Beemer, Christine A. Kelley

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

Abstract

We present a novel multidimensional network model as a means to analyze decoder failure and characterize trapping sets of graph-based codes. We identify a special class of these decoding networks, which we call transitive networks, and show how they may be used to identify trapping sets and inducing sets. Many codes have transitive decoding network representations. We conclude by investigating the decoding networks of codes arising from product, half-product, and protograph code constructions.

Original languageEnglish (US)
Title of host publicationCoding Theory and Applications - 5th International Castle Meeting, ICMCTA 2017,Proceedings
EditorsAngela I. Barbero, Vitaly Skachek, Oyvind Beck
PublisherSpringer Verlag
Pages11-20
Number of pages10
ISBN (Print)9783319662770
DOIs
StatePublished - 2017
Event5th International Castle Meeting on Coding Theory and Applications, ICMCTA 2017 - Vihula, Estonia
Duration: Aug 28 2017Aug 31 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Castle Meeting on Coding Theory and Applications, ICMCTA 2017
CountryEstonia
CityVihula
Period8/28/178/31/17

Keywords

  • Finite state machines
  • Iterative decoding
  • LDPC codes
  • Multidimensional decoding networks
  • Redundancy
  • Trapping sets

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Beemer, A., & Kelley, C. A. (2017). Multidimensional decoding networks for trapping set analysis. In A. I. Barbero, V. Skachek, & O. Beck (Eds.), Coding Theory and Applications - 5th International Castle Meeting, ICMCTA 2017,Proceedings (pp. 11-20). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10495 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66278-7_2