| Paper ID | SS-8.4 |
| Paper Title |
TOWARDS PRACTICAL NEAR-MAXIMUM-LIKELIHOOD DECODING OF ERROR-CORRECTING CODES: AN OVERVIEW |
| Authors |
Thibaud Tonnellier, McGill University, Canada; Marzieh Hashemipour-Nazari, Eindhoven University of Technology, Netherlands; Nghia Doan, Warren Gross, McGill University, Canada; Alexios Balatsoukas-Stimming, Eindhoven University of Technology, Netherlands |
| Session | SS-8: Near-ML Decoding of Error-correcting Codes: Algorithms and Implementation |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Near-ML Decoding of Error-correcting Codes: Algorithms and Implementation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
