An error correcting code type recognition technique based on a deep learning approach is proposed in this paper. This problem could be addressed in the context of non-cooperative communications or adaptive coding and modulation. Inspired by text classification, we proposed a convolutional neural network (CNN) model improved by embedding and block mechanism to classify the linear block code, convolutional code, and turbo code with the only knowledge of the noisy information streams. It achieves higher recognition performance than the algorithms which are based on traditional deep learning and rank calculation. Further results show that the performance is greatly affected by block length and the dimension of the embedding layer. In a nutshell, the CNN with block mechanism and embedding is a promising feature extraction and classification technique, and it is suitable for the recognition of different kinds of communication signals.

提出了一种基于深度学习的纠错码类型识别技术。可以在非合作通信或自适应编码和调制的情况下解决此问题。受到文本分类的启发，我们提出了一种通过嵌入和块机制改进的卷积神经网络（CNN）模型，以仅对嘈杂的信息流有所了解，对线性块代码，卷积代码和Turbo代码进行分类。与基于传统深度学习和等级计算的算法相比，它具有更高的识别性能。进一步的结果表明，性能受块长度和嵌入层尺寸的影响很大。简而言之，具有块机制和嵌入功能的CNN是一种很有前途的特征提取和分类技术，