Recognition of error correcting codes based on CNN with block mechanism and embedding

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.