Applicability of single- and two-hidden-layer neural networks in decoding linear block codes

Srdan Brkic, Predrag Ivanis, Bane Vasic

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

Abstract

In this paper, we analyze applicability of single- and two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense.

Original languageEnglish (US)
Title of host publication2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665425841
DOIs
StatePublished - 2021
Event29th Telecommunications Forum, TELFOR 2021 - Virtual, Belgrade, Serbia
Duration: Nov 23 2021Nov 24 2021

Publication series

Name2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings

Conference

Conference29th Telecommunications Forum, TELFOR 2021
Country/TerritorySerbia
CityVirtual, Belgrade
Period11/23/2111/24/21

Keywords

  • Error-floors
  • Linear block codes
  • Low-density parity-check codes
  • ML decoding
  • Neural networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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