Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2024-11-06 DOI:10.1109/LCOMM.2024.3492711

Alexander von Bank;Eike-Manuel Edelmann;Sisi Miao;Jonathan Mandelbaum;Laurent Schmalen

引用次数: 0

Abstract

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This letter 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.

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短块长度LDPC码的尖峰神经信念传播解码器

脉冲神经网络（snn）是一种基于事件的神经网络，可以实现高效的信号处理。本文提出了一种新的低密度奇偶校验（LDPC）码解码算法，该算法通过使用snn近似校验节点更新方程，将snn集成到信念传播（BP）解码中。对于（273,191）和（1023,781）有限几何LDPC码，所提出的解码器在高信噪比（SNRs）下优于和积解码器。解码器的误码率与逐次松弛的归一化和积译码相似。此外，新的解码操作不需要了解信噪比，使其对信噪比不匹配具有鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.