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Towards Practical Near-Maximum-Likelihood Decoding of Error-Correcting Codes: An Overview | IEEE Conference Publication | IEEE Xplore

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


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 r...Show More

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.
Date of Conference: 06-11 June 2021
Date Added to IEEE Xplore: 13 May 2021
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Conference Location: Toronto, ON, Canada

References

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