# Automatic Digital Modulation Classification Based on Curriculum Learning
## 4 Results and Discussion
### The Accuracy of MentorNet Classifier  
#### 4.1.1 Overall Accuracy of MentorNet Classifier Under Different SNRs  
Before investigating the performance of MentorNet classifier, the Baseline classifier was established and trained by four training set with different SNR ranges. Herein, samples in the training set and test set were the signals passing through additive white Gaussian channel without phase drift and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Symbol rate & 1 Msps \\ Carrier frequency & 2 MHz \\ Sample frequency & 64 MHz \\ Number of symbols & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modulation parameter.  
Figure 6: The diagrams of (**a**) training StudentNet under the supervision of MentorNet training and (**b**) testing the performance.  
frequency drift Therefore, SNR was the ratio between the amplitudes of Gaussian noise and signal. Then the performance of trained Baseline classifiers was measured on one test set with SNRs ranging from \(-20\) to \(18\) dB and the results are shown in Figure 7a. It is obvious that when the SNR of the training set was relatively high (such as \(10\)-\(18\) dB, Black line in Figure 7a), the Baseline classifier possessed higher classification accuracy, whereas an unsatisfactory performance occurred on the samples with low SNR in the test set. Unfortunately, once the SNR range of the training set broadened to \(-20\)-\(18\) dB (Purple line in Figure 7a), the performance of the Baseline classifier showed an improvement on samples with low SNR in the test set but deterioration on samples with high SNR in the test set. We suppose this phenomenon should be induced by the overfitting of StudentNet in the Baseline classifier. To overcome this problem, the MentorNet classifier was proposed and tested. The MentorNet classifier was trained by only one training set with \(-20\)-\(18\) dB SNR and its performance was verified on the same test set with the Baseline classifier. The green and magenta curves in Figure 7a revealed that for the training set with \(-20\)-\(18\) dB SNR, the MentorNet classifier could overcome the overfitting, and results in a \(1.7\%\) improvement in classification accuracy.  
Besides, we also compared the accuracy of the MentorNet classifier with several existing modulation classifiers, including the classifiers based on the Inception [26], the fusion model of convolutional neural network and long short-term memory (CNN-LSTM) [27], and SVM [27]. The five classifiers were trained and tested with the same training set and test set, and then the classification accuracy are shown in Figure 7b. Comparison results indicated that both the accuracy of the MentorNet classifier and Baseline classifier was higher than others, which verifies that ResNet could improve the classification accuracy significantly. Due to the existence of overfitting in the Baseline classifier, its performance was worse than the MentorNet classifier. Therefore, we can conclude that the MentorNet classifier proposed by us could achieve the higher classification accuracy.