Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
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DOI：
10.1109/isit.2019.8849419
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发表时间：
2019-01
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通讯作者：
Mengke Lian;Fabrizio Carpi;Christian Häger;H. Pfister
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文献类型：
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作者：
Mengke Lian;Fabrizio Carpi;Christian Häger;H. Pfister
我们考虑了Nachmani等人最近提出的加权信仰 - propapation（WBP）。减少存储和计算伯嫩的边缘的主要贡献是表明，很少有参数的简单缩放通常与完整的参数化相同。此外，提出了一些对WBP的训练改进，例如，就位错误率（BER）而言，将平均二进制跨透明拷贝降至最低，并提出了一个新的“软ber”损失为了更好的性能。具有高度冗余的奇偶校验检查矩阵的代码，训练一个具有软损失的锅的底盘可提供接近最大的样式性能，假设仅使用三个参数进行简单缩放。
We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al. where different weights are introduced for each Tanner graph edge and optimized using machine learning techniques. Our focus is on simple-scaling models that use the same weights across certain edges to reduce the storage and computational burden. The main contribution is to show that simple scaling with few parameters often achieves the same gain as the full parameterization. Moreover, several training improvements for WBP are proposed. For example, it is shown that minimizing average binary cross-entropy is suboptimal in general in terms of bit error rate (BER) and a new "soft-BER" loss is proposed which can lead to better performance. We also investigate parameter adapter networks (PANs) that learn the relation between the signal-to-noise ratio and the WBP parameters. As an example, for the (32, 16) Reed–Muller code with a highly redundant parity-check matrix, training a PAN with soft-BER loss gives near-maximum-likelihood performance assuming simple scaling with only three parameters.