GBPNet：用广义置信传播网络学习线性码译码
Learning to Decode Linear Codes with Generalized Belief Propagation Network
AI 导读
研究者提出广义置信传播网络 GBPNet，在保留消息传递对称性和 Tanner 图结构的同时，通过级联变量节点更新和可学习的逐元素消息缩放增强译码能力。论文还引入渐进训练和高信噪比训练两种策略提升译码准确度。在多种线性码上的实验显示，GBPNet 的误比特率低于传统置信传播及所评测的最先进神经译码器。
正文 · 原文
Abstract:Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms. In this paper, we propose a Generalized Belief Propagation Network (GBPNet) that preserves message-passing symmetry while enhancing the decoder's learning capability. The architecture uses cascaded variable-node updates and learned element-wise message scaling while retaining the Tanner graph structure. We also introduce two training strategies, progressive training and high-SNR training, that improve decoding accuracy. Experimental results on several linear codes show that GBPNet achieves lower bit error rates than conventional belief propagation and the evaluated state-of-the-art neural decoders.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06874 [cs.AI] |
| (or arXiv:2611.06874v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06874 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Samira Malek [view email]
[v1]
Wed, 4 Nov 2026 18:29:31 UTC (1,036 KB)
来源：arXiv cs.AI · arxiv.org