This paper considers the blind recognition of the type and the encoding parameters of channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural network (RNN), the attention mechanism, and the residual neural network (ResNet), three universal recognizers are proposed to identify the type, rate, and length of the target channel codes, with a training set generated by a small portion of all the possible code parameters. The proposed architectures need near zero a priori knowledge about the target channel code, and only require the length of the received signal to be dozen times of the codeword length. Numerical experiments show that the proposed deep learning methods own strong generalization to identify channel codes from the testing samples not generated by the encoding parameters utilized for the training set.

中文翻译：

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通过深度学习进行盲通道代码识别

本文考虑从高斯噪声信号中盲识别信道码的类型和编码参数。具体来说，基于循环神经网络（RNN）、注意力机制和残差神经网络（ResNet），提出了三种通用识别器来识别目标通道码的类型、速率和长度，并生成训练集通过所有可能的代码参数的一小部分。建议的架构需要接近零目标信道码的先验知识，只要求接收信号的长度是码字长度的几十倍。数值实验表明，所提出的深度学习方法具有很强的泛化能力，可以从不是由用于训练集的编码参数生成的测试样本中识别信道代码。