Approaching maximum likelihood performance of LDPC codes by stochastic resonance in noisy iterative decoders

Bane Vasić, Predrag Ivaniš, David Declercq, Khoa LeTrung

Research output: ResearchConference contribution

  • 3 Citations

Abstract

In the 1960s-70s, Taylor and Kuznetsov obtained a remarkable result that information can be reliably retrieved from a noisy channel even if a decoder is made of noisy components. The results of Vasic and Chilappagari presented at the ITA Workshop ten years ago have revived the interest in decoders made of noisy hardware and since then a number of improvements of the iterative decoders have been made to bring their performance closer to that of their perfect counterparts. However, a common mantra has been that noisy decoders cannot be better than their perfect counterparts. In this talk we report an unexpected phenomenon we have recently discovered - noise can actually improve the error correction process by reducing the probability of decoding error, in some cases by more that two orders of magnitude. This new form of stochastic resonance enables us to use logic gate errors to correct channel errors. This novelty recognizes that the decoder - essentially an iterative minimization of the Bethe free energy on the code graph - can get trapped in local minima, and random perturbations help the decoder to escape from these minima and converge to a correct code-word. In the spirit of Marcus Tullius Cicero's "Clavus clavo eicitur," ("one nail drives out another") they operate on the principle: Error errore eicitur" - "one error drives out another." Crucially, such useful random perturbations require neither additional hardware nor energy, as they are built into the low-powered, noisy hardware itself.

LanguageEnglish (US)
Title of host publication2016 Information Theory and Applications Workshop, ITA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509025299
DOIs
StatePublished - Mar 27 2017
Event2016 Information Theory and Applications Workshop, ITA 2016 - La Jolla, United States
Duration: Jan 31 2016Feb 5 2016

Other

Other2016 Information Theory and Applications Workshop, ITA 2016
CountryUnited States
CityLa Jolla
Period1/31/162/5/16

Fingerprint

Maximum likelihood
Hardware
Nails
Logic gates
Error correction
Free energy
Decoding

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Artificial Intelligence
  • Information Systems
  • Signal Processing

Cite this

Vasić, B., Ivaniš, P., Declercq, D., & LeTrung, K. (2017). Approaching maximum likelihood performance of LDPC codes by stochastic resonance in noisy iterative decoders. In 2016 Information Theory and Applications Workshop, ITA 2016 [7888185] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/ITA.2016.7888185

Approaching maximum likelihood performance of LDPC codes by stochastic resonance in noisy iterative decoders. / Vasić, Bane; Ivaniš, Predrag; Declercq, David; LeTrung, Khoa.

2016 Information Theory and Applications Workshop, ITA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7888185.

Research output: ResearchConference contribution

Vasić, B, Ivaniš, P, Declercq, D & LeTrung, K 2017, Approaching maximum likelihood performance of LDPC codes by stochastic resonance in noisy iterative decoders. in 2016 Information Theory and Applications Workshop, ITA 2016., 7888185, Institute of Electrical and Electronics Engineers Inc., 2016 Information Theory and Applications Workshop, ITA 2016, La Jolla, United States, 1/31/16. DOI: 10.1109/ITA.2016.7888185
Vasić B, Ivaniš P, Declercq D, LeTrung K. Approaching maximum likelihood performance of LDPC codes by stochastic resonance in noisy iterative decoders. In 2016 Information Theory and Applications Workshop, ITA 2016. Institute of Electrical and Electronics Engineers Inc.2017. 7888185. Available from, DOI: 10.1109/ITA.2016.7888185
Vasić, Bane ; Ivaniš, Predrag ; Declercq, David ; LeTrung, Khoa. / Approaching maximum likelihood performance of LDPC codes by stochastic resonance in noisy iterative decoders. 2016 Information Theory and Applications Workshop, ITA 2016. Institute of Electrical and Electronics Engineers Inc., 2017.
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