# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
###### Abstract
Hence, a new identification algorithm with low complexity and high fault-tolerance is needed, which will be applied to many types of forward error correction codes and is convenient for engineering and practical application. Deep learning is a subfield of machine learning. Traditional classifiers include linear discriminant analysis (LDA) [18], multi-level perception (MLP) [19] and support vector machine (SVM) [20]. As an emerging method, it has had a transformative impact on the development of natural language processing [21], computer vision [22] and other fields. Deep learning extracts features through multi-layer nonlinear compound operations, which also has a good data expression effect for complex data distribution. In terms of adaptive modulation and coding, it has also achieved satisfactory results [23; 24; 25; 26]. The core of deep learning method is hierarchical representation learning, which aims to extract more complex high-level features by combining low-level features, so as to solve the previous tough problems requiring manual design of features [27]. It requires a great number of sample data for training the obtained nonlinear function [28]. The classification accuracy of the function for unknown data is significant. So deep learning can be used to deal with many kinds of signal processing problems. In recent years, deep learning has been draw into the area of recognition of code types. In [29], the authors constructed a deep fusion model based on 1-D convolutional layer and modified 1-D inception architecture to recognize the types of FEC codes and in [30], the authors proposed a convolutional neural network (CNN) model improved by embedding and block mechanism to classify the FEC codes. It achieves higher recognition performance than the algorithms which are based on traditional deep learning. However, these algorithms succeed to recognize the code types in 10 dB or even higher SNR and fail to achieve the goal in low signal to noise ratio. In the paper, a code type recognition method based on RNN is raised. The novel method can identify the type of code through the classification process under low signal-to-noise ratio conditions. Furthermore, the advantage of this method lies in that any prior knowledge of encoder algorithm is not needed. Moreover, unlike traditional feature extraction methods, this method can automatically extract high-level features and improve the performance of FEC code type recognition. At the same time, compared with typical CNN, Simple RNN further considers the internal feature relationship of the code word, which improves the recognition accuracy. So as to improve the performance of Simple RNN furtherly, we come up with a weight initialization method. The results indicate that the improved RNN precedes Simple RNN or a single inception network. The major contributions of the paper are summed up as follows. 1. Aiming at FEC codes in non-cooperative communication system, a blind type recognition method based on RNN is proposed. 2. We adapted a new weight initialization method in RNN network to improve training efficiency and training accuracy and achieved end-to-end extraction of features. The gradient disappearance is effectively suppressed, the computational complexity is reduced and the training efficiency is improved. 3. We constructed a comprehensive and in-depth study on the performance of FEC coding type recognition on account of recurrent neural network. Experimental results indicate that compared with traditional networks and other single neural networks under low signal-to-noise ratio conditions, the performance of the Improved RNN is remarkably improved.