# Automatic Digital Modulation Classification Based on Curriculum Learning
## 1 Introduction  
An automatic modulation classification task aims at detecting the modulation type of a received signal and recovering the signal by demodulation. Currently, it has been widely used in military electronic warfare, surveillance and threat analysis [1; 2]. The likelihood-based (LB) method [3] and feature-based (FB) method [4] are two conventional methods for automatic modulation classification. LB method mainly includes the average likelihood ratio test (ALRT) method [5] and the generalized likelihood ratio test (GLRT) [6]. Although the LB method obtains high accuracy, it requires more calculating time to fulfill parameter estimation, which greatly limits its application [7]. FB methods usually work in two steps: Feature extraction and classification. In previous papers based on FB methods, many signal features, such as spectrum [8], high-order cumulant [9] and wavelet coefficients [10], are used to classify the modulation types. With the emergence and development of machine learning (ML), many researches employ ML to implement classification in FB method. For examples, [PERSON] et al. [11] reported a modulation classifier based on genetic programming and K-nearest neighbor (GP-KNN), but this classifier only worked well for PSK. [PERSON] et al. [12] employed the support vector machine (SVM) to classify the phase shift keying (PSK) and quadrature amplitude modulation (QAM) and obtained a good classification accuracy under the known channel. Although the FB method shows great advantages inautomatic digital modulation classification, there are still two challenges: Artificial feature extraction and noise covering. The performance of FB methods severely depends on the quality and quantity of extracted features, but the artificial feature extraction is complex and difficult for various modulated wireless signals. Moreover, when the signal-noise ratio (SNR) of the modulated signal is very low, the performance of classifier is unsatisfied due to the limited quantity of features extracted. The neural network [13] is a fascinating classification method with a series of state-of-the-art achievements automatic modulation classification [14; 15]. For instance, [PERSON] et al. [16] trained a deep neural network (DNN) using a baseband IQ waveform to identify modulation. They reported that it was feasible to use DNN for automatic modulation classification and had a better accuracy with low SNR. [PERSON] et al. [17] verified the classification performance of long short-term memory (LSTM), convolutional long short-term memory deep neural network (CLDNN) and deep residual network (ResNet) structures. Experimental results showed that the three methods could achieve good classification results on the dataset RadioML2016.10b [16]. The paper also verified the impact of training data with different SNRs, and minimized the training data to reduce training time. However, a neural network is very easy to overfit and memorize data noise when using it in modulation classification [18]. Noises will be introduced into the signal when it goes through channels, inducing a sharp decrease in SNR. If this low SNR data is used to train the neural network, local optimum could appear and cause significant decline in the performance of classifier. To solve the overfitting of neural network, we propose a novel automatic digital modulation classifier with two neural networks, namely the StudentNet and MentorNet. The StudentNet is used to classify the signal, and the MentorNet is employed to supervise the training of StudentNet according to curriculum learning. Experimental results show that our classifier can accurately identify 11 common digital modulated signals, including 2-ary amplitude shift keying (2 ASK), 2-ary Frequency Shift Keying (2 FSK), 2 PSK, 4 ASK, 4 FSK, 4 PSK, 8 ASK, 8 FSK, 8 PSK, 16 QAM and 64 QAM. The overall classification accuracy can be up to 99.3%, which is much higher than other classifiers. The structure of this paper is organized as follows. Section 2 shows the signal model and relative theories.