基于广义置信传播网络的线性码解码学习
Learning to Decode Linear Codes with Generalized Belief Propagation Network
Samira Malek
AI 速读 基于摘要
提出保持消息传递对称性的广义置信传播网络 GBPNet，并引入渐进训练与高信噪比训练两种策略，作者报告其在多种线性码上取得比传统 BP 和所评估的先进神经解码器更低的误码率。
cs.AI
查看原文摘要
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.