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.

在信号拦截和智能通信系统中，盲目识别信道代码变得越来越重要。但是，大多数现有的通道代码识别算法都是手动提取特征的，这使得它们在实际应用中具有很高的要求。因此，使用现有技术很难有效地识别信道代码。本文针对给定的软决策序列，针对卷积码参数的盲识别提出了一种基于深度残差网络的深度学习（DL）方法。所提出的方法可以在不需要编码参数的先验信息的情况下盲目识别卷积码，当SNR大于或等于零时，它对17种形式的卷积码的识别精度达到88％以上。此外，我们研究了影响通道代码识别准确性的因素，包括输入长度，模型深度和数据类型。然后，对所提算法，基于对数似然比（LLR）的传统盲识别算法和基于DL的算法之间的识别精度进行了比较。实验结果表明，在卷积码的盲识别中，基于深度残差网络的方法可以对传统算法或现有基于DL的算法进行重大改进。