Iterative decoding beyond belief propagation

Shiva Kumar Planjery, Shashi Kiran Chilappagari, Bane Vasić, David Declercq, Ludovic Danjean

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

9 Scopus citations


At the heart of modern coding theory lies the fact that low-density parity-check (LDPC) codes can be efficiently decoded by belief propagation (BP). The BP is an inference algorithm which operates on a graphical model of a code, and lends itself to low-complexity and high-speed implementations, making it the algorithm of choice in many applications. It has unprecedentedly good error rate performance, so good that when decoded by the BP, LDPC codes approach theoretical limits of channel capacity. However, this capacity approaching property holds only in the asymptotic limit of code length, while codes of practical lengths suffer abrupt performance degradation in the low noise regime known as the error floor phenomenon. Our study of error floor has led to an interesting and surprising finding that it is possible to design iterative decoders which are much simpler yet better than belief propagation! These decoders do not propagate beliefs but a rather different kind of messages that reflect the local structure of the code graph. This has opened a plethora of exciting theoretical problems and applications. This paper introduces this new paradigm.

Original languageEnglish (US)
Title of host publication2010 Information Theory and Applications Workshop, ITA 2010 - Conference Proceedings
Number of pages10
StatePublished - 2010
Event2010 Information Theory and Applications Workshop, ITA 2010 - San Diego, CA, United States
Duration: Jan 31 2010Feb 5 2010

Publication series

Name2010 Information Theory and Applications Workshop, ITA 2010 - Conference Proceedings


Other2010 Information Theory and Applications Workshop, ITA 2010
Country/TerritoryUnited States
CitySan Diego, CA

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems


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