导读
英文标题：Learning to Decode Linear Codes with Generalized Belief Propagation Network arXiv编号：2611.06874 作者：Samira Malek 来源：arXiv 原文链接：https://arxiv.org/ab…
英文标题：Learning to Decode Linear Codes with Generalized Belief Propagation Network
arXiv编号：2611.06874
作者：Samira Malek
来源：arXiv
原文链接：https://arxiv.org/abs/2611.06874
中文摘要：
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.
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