## 1 Introduction  
Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. The aim of AMC is to identify the modulation type of received signal correctly and automatically, which reveals a broad application foreground both in civil and military technologies [1]. In civilian aspects, AMC can be applied to ensure the normal communication of legitimate users by monitoring legitimate spectrum and identifying illegitimate interference. AMC can also be used to reinforce and foster situational awareness in soft-defined radio system for better spectrum utilization [2]. In a non-orthogonal multiple access system, AMC can identify the modulation type of non-orthogonal multiple access system and decide whether successive interference cancellation is required or not [3]. In military aspects, AMC is the core technology of electronic counter-measures, communication surveillance and jamming. AMC can help intercept receivers to get the correct modulation type, which provides a reference basis for demodulation algorithm selection of demodulator and is helpful to the selection of optimal jamming pattern or jamming cancellation algorithm for electronic warfare, so as to ensure friendly communication and suppress and destroy enemy communication, so as to achieve the purpose of electronic warfare communication countermeasure [4]. Consequently, it is absolutely imperative to develop AMC technology. External literature can be divided into two categories: maximum likelihood-based and feature-based approaches. Maximum likelihood-based approaches can be divided into 3 categories: average likelihood ratio test (ALRT) [5], generalised likelihood ratio test (GLRT) [6] and hybrid likelihood ratio test (HLRT) [7]. For maximum likelihood-based approaches, AMC problems can be reviewed as a hypothesis testing problem. Specifically, according to the statistical characteristics of the signal, different statistics for classification are calculated, and then compared with an appropriate threshold to form a decision rule. The most popular statistics are average likelihood radio, generalised likelihood ratio and hybrid likelihood ratio. Maximum likelihood-based approaches can get the optimal solutions theoretically, but they suffer from high computational complexity and require much prior information. These shortcomings greatly restrict their application. In contrast, feature-based approaches can achieve suboptimal solutions with much lower computational complexity and less prior information, which make feature-based approaches more practical. Feature-based approaches can be divided into traditional AMC methods and deep learning methods. The features traditional AMC methods utilized are handcraft features and the classifiers usually choose traditional machine learning methods such as support vector machine [8], decision tree [9] and K-neighbourhood algorithms [10]. The most popular handcraft features are High-Order Cumulant features [11], cyclic spectrum features [12] and Wigner Ville distribution features [13]. Whereafter, different machine learning methods are used for classification. There are also some non-machine learning methods for AMC. [PERSON] [14] pre-computed thresholds for AMC in the presence of high frequency noise. [PERSON] [15] calculated thresholds for inter-class recognition and applied expectation-maximization method for intra-class recognition. However, handcraft features only show characteristics of one specific domain and simple threshold methods cannot handle complex situations. If we need more accurate description of signal, we have to select different types of handcraft features and concatenate them for classification [16]. However, when facing lower Signal to Noise Ratio (SNR) and more modulation types, handcraft features are not enough for classification. Thus, we need more powerful AMC algorithms. The proliferation of deep learning has recently leaped into the modulation classification. Deep learning has been proposed in [17], which proposed deep Convolution Neural Network (CNN).