Pruning Neural Belief Propagation Decoders
Paper in proceedings, 2020

We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this method significantly outperforms conventional BP decoding, the underlying parity-check matrix may still limit the overall performance. In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We consider the weights in the Tanner graph as an indication of the importance of the connected check nodes (CNs) to decoding and use them to prune unimportant CNs. As the pruning is not tied over iterations, the final decoder uses a different parity-check matrix in each iteration. For ReedMuller and short low-density parity-check codes, we achieve performance within 0.27dB and 1.5dB of the ML performance while reducing the complexity of the decoder.

Author

Andreas Buchberger

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Christian Häger

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Henry D. Pfister

Duke University

Laurent Schmalen

Karlsruhe Institute of Technology (KIT)

Alexandre Graell I Amat

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

IEEE International Symposium on Information Theory - Proceedings

21578095 (ISSN)

Vol. 2020-June 338-342 9174097

2020 IEEE International Symposium on Information Theory, ISIT 2020
Los Angeles, USA,

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Subject Categories

Telecommunications

Signal Processing

Computer Systems

DOI

10.1109/ISIT44484.2020.9174097

More information

Latest update

9/18/2020