一种用于信道解码的迭代BP-CNN结构
An Iterative BP-CNN Architecture for Channel Decoding
摘要：
受深度学习最新进展的启发，我们提出了一种新的迭代BP-CNN架构，用于相关噪声下的信道解码。该架构将训练的卷积神经网络（CNN）与标准置信传播（BP）解码器连接在一起。标准BP解码器用于估计编码比特，随后是CNN以去除BP解码器的估计误差并获得信道噪声的更准确估计。BP和CNN之间的迭代将逐渐提高解码SNR，从而导致更好的解码性能。为了训练性能良好的CNN模型，我们定义了一个新的损失函数，它不仅涉及噪声估计的准确性，而且还涉及估计误差的正态性检验，即，以测量估计误差遵循高斯分布的可能性。与使用标准二次损失函数相比，将正态性检验引入到CNN训练中形成了残余噪声分布，并进一步降低了迭代解码的BER。我们进行了大量的实验来分析和验证所提出的框架。迭代BP-CNN解码器具有更好的BER性能和更低的复杂度，适合并行实现，不依赖于任何特定的信道模型或编码方法，并且对训练失配具有鲁棒性。所有这些特点使它成为一个很好的候选解码现代信道码。
英文摘要：
Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and hence result in better decoding performance. To train a well-behaved CNN model, we define a new loss function which involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the BER of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework. The iterative BP-CNN decoder has better BER performance with lower complexity, is suitable for parallel implementation, does not rely on any specific channel model or encoding method, and is robust against training mismatches. All of these features make it a good candidate for decoding modern channel codes.
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