2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | C.3.4 | ||
| Paper Title | Pruning Neural Belief Propagation Decoders | ||
| Authors | Andreas Buchberger, Christian Häger, Chalmers University of Technology, Sweden; Henry Pfister, Duke University, United States; Laurent Schmalen, Karlsruhe Institute of Technology (KIT), Germany; Alexandre Graell i Amat, Chalmers University of Technology, Sweden | ||
| Session | C.3: Iterative Decoding | ||
| Presentation | Lecture | ||
| Track | Coding for Communications | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | 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 Reed-Muller and short low-density parity-check codes, we achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
