Towards Practical Near-Maximum-Likelihood Decoding of Error-Correcting Codes: An Overview
Thibaud Tonnellier, Marzieh Hashemipour, Nghia Doan, Warren J. Gross, Alexios Balatsoukas-Stimming
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.
BibTeX
@inproceedings{icassp2021_towardspractical,
title = {Towards Practical Near-Maximum-Likelihood Decoding of Error-Correcting Codes: An Overview},
author = {Thibaud Tonnellier and Marzieh Hashemipour and Nghia Doan and Warren J. Gross and Alexios Balatsoukas-Stimming},
booktitle = {ICASSP 2021},
year = {2021}
}