GBPNet：用广义置信传播网络解码线性码，误码率低于现有神经解码器
Learning to Decode Linear Codes with Generalized Belief Propagation Network
通信方向的朋友可以看看：把神经网络接进置信传播解码器，误码率比 BP 和现有神经解码器都低，还给了两个训练技巧。
论文提出 Generalized Belief Propagation Network（GBPNet），在保留 Tanner 图消息传递对称性的同时，加入级联变量节点更新和可学习的逐元素消息缩放。作者还设计了渐进式训练和高信噪比训练两种策略来提升解码精度。在多个线性码的实验中，GBPNet 的误码率低于传统置信传播算法和已评估的最先进神经解码器。
Learning to Decode Linear Codes with Generalized Belief Propagation Network
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.