## 1 Introduction  
Automatic modulation recognition (AMR) has been an important topic in wireless communication. AMR is essential in radio fault detection, spectrum interference monitoring, and a wide variety of military and civilian applications. Traditional AMR methods, as explored in [1; 2; 3], employed decision theory and statistical pattern recognition. Most of the maximum likelihood methods based on hypothesis testing have higher computational complexity and are more sensitive to model mismatch problems, which greatly limits their application in wild communication environments. Other methods used manual feature extraction combined with machine learning (ML) to apply classification, as explored in [4; 5; 6]. These methods based on feature extraction and likelihood are effective in certain scenarios. Under certain conditions, the feature-based method can achieve the best recognition performance close to the theoretical limit, and it has strong robustness, so it is more widely used. A feature of these methods is dependence on expert knowledge and signal preprocessing. In many cognitive radio (CR) [7; 8; 9] and spectrum detection applications, fewer expert design and knowledge of signal captures mean improving real-time response and automatic processing capabilities. Improvingof these capabilities is one of the optimization directions of the AMR system. With the application of deep learning, classification directly on the raw IQ signals has achieved some encouraging results. Recently, deep learning (DL) has achieved outstanding results in the domains of natural language processing (NLP) [10], knowledge mapping, computer vision (CV) [11], speech signal processing [12], and intelligent medical diagnostics [13; 14]. The concept of deep learning originated from the study of artificial neural networks. Deep learning (DL) simulates the deep structure of the human brain, and the cognitive process is carried out layer by layer, and gradually realizes the hierarchical expression of the input information. Given the result of deep learning in other fields, DL can be combined with hardware to improve the upper performance limit of traditional algorithms, and can reduce the over-fitting to improve the robustness of the model through specific regularization methods. Therefore, DL has recently become a research hotspot in the communication domain, and its application in the field of AMR has also received widespread attention. The use of deep learning methods greatly reduces the reliance on expert knowledge. Through the powerful feature extraction ability of the DL model, the intrinsic connection and law of the sample data can be adaptively found, which can improve the performance of traditional modulation recognition methods. Thanks to the huge parameters of the deep learning model, the algorithm has strong fault tolerance and can achieve better generalization ability when dealing with distortion and noise-contaminated data, which is beneficial for coping with the challenges of complex nonlinear distortion, such as channel effects, receiver hardware noise, etc. Besides, the deep learning model has excellent inductive migration capabilities, and this ability can be applied to the field of modulation recognition to improve the cognitive recognition systems' ability to recognize new and more complex signals. In the field of signal processing, researchers are enthusiastically to apply deep learning methods to AMR. [PERSON] et al. [15] evaluate the classification performance of fractal dimension extraction methods combined with pattern recognition algorithm. The effects of random forests, back-propagation (BP), etc. applying to AMR were evaluated in experiments. [PERSON] et al. [16; 17; 18] introduced Convolutional Neural Network (CNN) to AMR domain, by directly applying classification with the time domain in-phase and quadrature (IQ) signal captures. He has also done extensive research on network design and optimization for AMR.