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Pruning and Quantizing Neural Belief Propagation Decoders

Buchberger, Andreas; Hager, Christian; Pfister, Henry D.; Schmalen, Laurent; Graell i Amat, Alexandre


We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding recently introduced by Nachmani et al. in which we allow a different parity-check matrix in each iteration of the algorithm. The key idea is to consider NBP decoding over an overcomplete parity-check matrix and use the weights of NBP as a measure of the importance of the check nodes (CNs) to decoding. The unimportant CNs are then pruned. In contrast to NBP, which performs decoding on a given fixed parity-check matrix, the proposed pruning-based neural belief propagation (PB-NBP) typically results in a different parity-check matrix in each iteration. For a given complexity in terms of CN evaluations, we show that PB-NBP yields significant performance improvements with respect to NBP. We apply the proposed decoder to the decoding of a Reed-Muller code, a short low-density parity-check (LDPC) code, and a polar code. PB-NBP outperforms NBP decoding over an overcomplete parity-check matrix by 0.27-0.31 dB while reducing the number of required CN evaluations by up to 97%. ... mehr

Zugehörige Institution(en) am KIT Communications Engineering Lab (CEL)
Publikationstyp Forschungsbericht/Preprint
Publikationsmonat/-jahr 07.2021
Sprache Englisch
Identifikator KITopen-ID: 1000135810
Nachgewiesen in arXiv
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