Pruning and Quantizing Neural Belief Propagation Decoders
Journal article, 2021

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%. For the LDPC code, PB-NBP outperforms conventional belief propagation with the same number of CN evaluations by 0.52 dB. We further extend the pruning concept to offset min-sum decoding and introduce a pruning-based neural offset min-sum (PB-NOMS) decoder, for which we jointly optimize the offsets and the quantization of the messages and offsets. We demonstrate performance 0.5 dB from ML decoding with 5-bit quantization for the Reed-Muller code.

neural decoders

pruning

min-sum decoding

quantization

Belief propagation

deep learning

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 Journal on Selected Areas in Communications

0733-8716 (ISSN)

Vol. 39 7 1957-1966

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/JSAC.2020.3041392

More information

Latest update

8/7/2021 1