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.

提出了一种新颖的基于 BP-CNN 架构的迭代解码方法，通过在标准 BP 解码器后添加卷积神经网络 (CNN) 来去除解码错误和噪声，以提高解码效果。训练 CNN 模型使用了新的损失函数，更好地处理了残差噪声分布，提高了误码率性能。该算法具有适用于并行实现、不依赖特定信道模型或编码方法，以及对训练失调有很强的鲁棒性等优点，可用于解码现代信道编码。

一种迭代的 BP-CNN 信道解码架构

An Iterative BP-CNN Architecture for Channel Decoding

The problem of low complexity, close to optimal, channel decoding of linear
codes with short to moderate block length is considered. It is shown that deep
learning methods can be used to improve a standard belief propagation decoder,
despite the large example space. Similar improvements are obtained for the
min-sum algorithm. It is also shown that tying the parameters of the decoders
across iterations, so as to form a recurrent neural network architecture, can
be implemented with comparable results. The advantage is that significantly
less parameters are required. We also introduce a recurrent neural decoder
architecture based on the method of successive relaxation. Improvements over
standard belief propagation are also observed on sparser Tanner graph
representations of the codes. Furthermore, we demonstrate that the neural
belief propagation decoder can be used to improve the performance, or
alternatively reduce the computational complexity, of a close to optimal
decoder of short BCH codes.

探讨利用深度学习方法改进信道解码器进行线性码的近似最优的解码，发现可以通过构建循环神经网络的体系结构来实现解码器参数的绑定，以此有效降低参数数量并提升性能。