Pruning and Quantizing Neural Belief Propagation Decoders

TL;DR

This paper introduces a pruning and quantization method for neural belief propagation decoders, significantly improving decoding performance and efficiency for various short linear block codes.

Contribution

It proposes a novel pruning-based neural belief propagation (PB-NBP) approach that adapts the parity-check matrix during decoding, enhancing performance over existing NBP methods.

Findings

PB-NBP outperforms NBP by 0.27-0.31 dB with fewer CN evaluations.

PB-NBP surpasses conventional belief propagation by 0.52 dB at same complexity.

Pruning and quantization enable near-ML decoding performance with low-bit messages.

Abstract

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.