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 language | English |
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| Title of host publication | 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021) |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 8283-8287 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-7281-7605-5 |
| DOIs | |
| Publication status | Published - 13 May 2021 |
| Event | 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021 https://2021.ieeeicassp.org/ |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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| ISSN (Print) | 1520-6149 |
Conference
| Conference | 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 |
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| Abbreviated title | ICASSP 2021 |
| Country/Territory | Canada |
| City | Toronto |
| Period | 6/06/21 → 11/06/21 |
| Internet address |
Keywords
- GRAND
- Linear programming decoding
- Machine-learning aided decoding
- Maximum-likelihood decoding
- Recursive projection-aggregation decoding