High quality data is essential in deep learning to train a robust model. While in other fields data is sparse and costly to collect, in error decoding it is free to query and label thus allowing potential data exploitation. Utilizing this fact and inspired by active learning, two novel methods are introduced to improve Weighted Belief Propagation (WBP) decoding. These methods incorporate machine-learning concepts with error decoding measures. For BCH(63,36), (63,45) and (127,64) codes, with cycle-reduced parity-check matrices, improvement of up to 0.4dB at the waterfall region, and of up to 1.5dB at the error-floor region in FER, over the original WBP, is demonstrated by smartly sampling the data, without increasing inference (decoding) complexity. The proposed methods constitutes an example guidelines for model enhancement by incorporation of domain knowledge from error-correcting field into a deep learning model. These guidelines can be adapted to any other deep learning based communication block.

中文翻译：

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线性代码的主动深度解码 Ishay Be'ery、Nir Raviv、Tomer Raviv、Yair B'eery

高质量数据对于深度学习训练鲁棒模型至关重要。虽然在其他领域数据稀疏且收集成本高，但在错误解码中，它可以自由查询和标记，从而允许潜在的数据利用。利用这一事实并受主动学习的启发，引入了两种新方法来改进加权置信传播 (WBP) 解码。这些方法将机器学习概念与错误解码措施相结合。对于 BCH(63,36)、(63,45) 和 (127,64) 代码，使用周期减少的奇偶校验矩阵，瀑布区改善高达 0.4dB，误差改善达 1.5dB FER 中的 -floor 区域，在原始 WBP 之上，通过巧妙地采样数据来证明，而不会增加推理（解码）复杂性。所提出的方法通过将来自纠错领域的领域知识整合到深度学习模型中，构成了模型增强的示例指南。这些指南可以适用于任何其他基于深度学习的通信块。