Blind identification of convolutional codes based on deep learning

Blind identification of channel codes is becoming increasingly important in signal interception and intelligent communication systems. However, most existing channel codes recognition algorithms extract features manually, which makes them highly demanding in real-world application. Thus, efficiently identifying channel codes is difficult using present technologies. This paper presents a deep residual network-based deep learning (DL) approach on the blind identification of convolutional code parameters for a given soft-decision sequence. The proposed method can blindly identify the convolutional codes without the need for the prior information about its coding parameters, and it achieves over 88% of recognition accuracy for 17 forms of convolutional codes when SNR exceeds or equals zero. Furthermore, we investigate factors affecting the accuracy of channel codes recognition including input length, model depth and data type. A comparison of the recognition accuracy between the proposed algorithm, log-likelihood ratio (LLR)-based traditional blind identification algorithm, and DL-based algorithm are then made. Experiment results show that deep residual network-based approaches could provide significant improvements over the traditional algorithm or existing DL-based algorithms in the blind identification of convolutional codes. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Blind identification of channel codes is crucial in signal interception and intelligent communication systems. This paper proposes a deep residual network-based deep learning approach that achieves high recognition accuracy for various forms of convolutional codes without prior information. Experimental results demonstrate that this method outperforms traditional algorithms and existing DL-based algorithms in blind identification of convolutional codes.