Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes

Spiking neural networks (SNNs) are neural
networks that enable energy-efficient signal processing due to
their event-based nature. This letter proposes a novel decod-
ing algorithm for low-density parity-check (LDPC) codes that
integrates SNNs into belief propagation (BP) decoding by approx-
imating the check node update equations using SNNs. For the
(273,191) and (1023,781) finite-geometry LDPC code, the pro-
posed 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 relax-
ation. Furthermore, the novel decoding operates without requir-
ing knowledge of the SNR, making it robust to SNR mismatch.