学习用广义置信传播网络解码线性码
Learning to Decode Linear Codes with Generalized Belief Propagation Network
Samira Malek
中文摘要
纠错码是通信系统的基本组成部分，可在噪声信道上实现可靠数据传输。近期，神经解码器相较经典迭代算法展现出显著性能提升。提出一种广义置信传播网络（GBPNet），在保留消息传递对称性的同时增强解码器的学习能力。该架构采用级联变量节点更新与学习到的逐元素消息缩放，并保留 Tanner 图结构。还引入渐进训练与高信噪比训练两种训练策略，以提高解码准确率。在多种线性码上的实验结果表明，GBPNet 的误码率低于传统置信传播及所评估的当前最优神经解码器。
关键要点
- 01神经解码器已优于经典迭代算法，但需在保留消息传递对称性的同时增强学习能力
- 02GBPNet 采用级联变量节点更新与学习到的逐元素消息缩放，保留 Tanner 图结构
- 03引入渐进训练与高信噪比训练两种策略提升解码准确率
- 04在多种线性码上误码率低于传统置信传播与所评估的当前最优神经解码器
解读
尚无解读。
原始英文摘要
arXiv:2611.06874v1 Announce Type: new 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.