2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSS-8.3
Paper Title ADMM-BASED ML DECODING: FROM THEORY TO PRACTICE
Authors Kira Kraft, Norbert Wehn, Technische Universität Kaiserslautern, Germany
SessionSS-8: Near-ML Decoding of Error-correcting Codes: Algorithms and Implementation
LocationGather.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
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.