Towards Practical Near-Maximum-Likelihood Decoding of Error-Correcting Codes: An Overview

Thibaud Tonnellier, Marzieh Hashemipour Nazari, Nghia Doan, Warren J. Gross, Alexios Balatsoukas-Stimming

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

While in the past several decades the trend to go towards increasing error-correcting code lengths was predominant to get closer to the Shannon limit, applications that require short block length are developing. Therefore, decoding techniques that can achieve near-maximum-likelihood (near-ML) are gaining momentum. This overview paper surveys recent progress in this emerging field by reviewing the GRAND algorithm, linear programming decoding, machine-learning aided decoding and the recursive projection-aggregation decoding algorithm. For each of the decoding algorithms, both algorithmic and hardware implementations are considered, and future research directions are outlined.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)
PublisherInstitute of Electrical and Electronics Engineers
Pages8283-8287
Number of pages5
ISBN (Electronic)978-1-7281-7605-5
DOIs
Publication statusPublished - 13 May 2021
Event2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021) - Virtual conference, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021
https://2021.ieeeicassp.org/

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)
Abbreviated titleICASSP 2021
Country/TerritoryCanada
CityToronto
Period6/06/2111/06/21
Internet address

Keywords

  • GRAND
  • Linear programming decoding
  • Machine-learning aided decoding
  • Maximum-likelihood decoding
  • Recursive projection-aggregation decoding

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