# Automatic Digital Modulation Classification Based on Curriculum Learning
## 4 Results and Discussion
### The Accuracy of MentorNet Classifier
#### 4.1.2 Intra-Class and Inter-Class Accuracy of the MentorNet Classifier Under Different SNRs  
In addition to the overall accuracy, the intra-class and inter-class accuracy of the classifier is also worthy to mention. The common modulation signals can be divided into four classes including ASK, FSK, PSK and QAM according to the modulation method. According to the modulation order, these four classes also can be divided into eleven types, including 2 ASK, 4 ASK, 8 ASK, 2 FSK, 4 FSK, 8 FSK, 2 PSK, 4 PSK, 8 PSK, 16 QAM and 64 QAM. The intra-class accuracy of MentorNet classifier for each modulation class at different SNR is reported in Figure 8, which denotes that all classification accuracy increased with SNR until approaches closed to \(100\%\). In details, when SNR was larger than \(-10\) dB, the classification accuracy of 2 ASK was largest in ASK and saturated at \(10\) dB. Meantime,  
Figure 7: The performance of various classifiers under different SNRs: (**a**) Curves about the classification accuracy versus the SNR range of the training set, and (**b**) classification accuracy of different methods with \(-20\)–\(18\) dB SNR.  
the classification accuracy of 2 PSK was also the largest in PSK and saturated at \(-\)10 dB. Besides, the modulation order had few impacts on the classification accuracy of FSK as SNR was lower than 0 dB. However, the intra-class accuracy of QAM was almost unaffected by the modulation order. These results suggest that the modulation order has a different influence on the intra-class accuracy of different classes.  
The inter-class accuracy of MentorNet classifier was obtained by its confusion matrix as shown in Figure 9. The confusion matrix illustrates the prediction error of the classifier, where the horizontal and vertical axes represent the actual and predicted modulation types. The inter-class accuracy was calculated by ignoring the modulation order and adding the probability of achieving the correct modulation class. From Figure 9, we can conclude that it was difficult to identify both the modulation order and the modulation class accurately at low SNR (such as \(-\)20 dB) due to the large noise interference, which is consistent with Figures 7 and 8. It is well-known that the wrong modulation order cannot pose a fatal threat to the demodulated signal so that the demodulated signal showed a large deviation with the original signal. The correct modulation class was the most urgent need for us. Hence, we presented the inter-class accuracy of MentorNet classifier in Figure 10. As shown in Figure 10, the MentorNet classifier could effectively distinguish modulation classes such as ASK, FSK and PSK even if SNR was very low (such as \(-\)20 dB). However, the inter-class accuracy of QAM was relatively low as SNR was lower than \(-\)10 dB, because QAM was easy to be recognized as PSK according to Figure 9. However, the original signal of QAM can be recovered by conventional demodulation in the case of misjudgment. Therefore, the performance of the MentorNet classifier could satisfy the accuracy requirements for modulation recognition in most applications.  
Figure 8: Curves about intra-class classification accuracy versus SNR.