| Paper ID | SS-8.3 |
| Paper Title |
ADMM-BASED ML DECODING: FROM THEORY TO PRACTICE |
| Authors |
Kira Kraft, Norbert Wehn, Technische Universität Kaiserslautern, Germany |
| 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 |
Integer Linear Programming (ILP) is a general method to solve the Maximum-Likelihood (ML) decoding problem for all kinds of binary linear codes. To this end, state-of-the-art techniques use a Branch-and-Bound (B&B) framework to partition the underlying integer linear problem into several relaxed linear problems. These linear problems then have to be solved in reasonable time by an efficient Linear Programming (LP) solver. Recently, the Alternating Direction Method of Multipliers (ADMM) has been proposed for efficient software and hardware LP decoding of sparse codes, hence, an ADMM-based ML decoder seems to be a promising approach. In this paper, we investigate this approach with respect to its algorithmic and implementation-specific challenges. |
