Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch.
@article{arxiv.2410.11543,
title = {Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes},
author = {Alexander von Bank and Eike-Manuel Edelmann and Sisi Miao and Jonathan Mandelbaum and Laurent Schmalen},
journal= {arXiv preprint arXiv:2410.11543},
year = {2024}
}