## 4 Experiment and Result Analysis
### Results of SEI Experiment on the ESP20 Dataset  
(1) Analysis of comprehensive performance indicators.This section analyzes the evaluated comprehensive indicators and robustness of the proposed method for the ESP20 dataset. Table 2 lists the experimental results of different evaluation indicators of the proposed method and other comparison methods at various SNRs. The optimum results have been bolded. As shown in Table 2, ENN had optimum performance in four evaluation indicators compared with other comparison methods. GCN performed second in the indicators, which was better than CNN. Through a comparison between GCN and CNN, it is concluded that the signal graph proposed in this paper is effective for signal data representation. Moreover, ENN performed better than CNN and GCN in SEI, which indicated that ENN can effectively extract the fingerprint feature of a signal and identify the specific emitter. Figure 4 shows the confusion matrices of the methods, where the pixels of ENN concentrate on the diagonal clearly, while those of the confusion matrix of the comparison methods are distributed beyond the diagonal area vaguely. This case intuitively explains that the ENN has the optimum SEI performance. (2) Robustness analysis at various SNRs.As shown in Table 2, the classification indicators of the ENN were obviously superior to those of CNN and GCN at different SNRs. Figure 5 illustrates that the proposed method has higher accuracy than the comparison methods at all the SNRs. This proves that this method has excellent robustness at different SNRs. Especially in poor signal environments, for example, when the SNR was 20 dB, the experimental results better reflected the robustness of the proposed method. At this time, the identification accuracy of CNN was 76.38%, that of GCN was 84.51%, and that of the proposed method was 85.23%. The accuracy of GCN based on signal graph and improved graph convolution was about 8% higher than that of CNN, which implied that signal graph was effective for representing signal data. Meanwhile, the accuracy of ENN was about 1% higher than that of GCN, further indicating the excellent robustness of ENN. Figure 4: Confusion matrices of ENN and the comparison methods. The confusion matrix of our model has been highlighted with a red square. The stronger the blue color, the higher the classification accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & **Index** & **ENN** & **CNN** & **GCN** & **KNN** & **TREE** \\ \hline \multirow{4}{*}{raw} & Accuracy & **97.38** & 94.4 & 95.78 & 79.91 & 62.9 \\  & Precision & **97.33** & 93.76 & 94 & 80.61 & 63.17 \\  & Recall & **97.41** & 93.56 & 94.45 & 79.79 & 62.77 \\  & F1-Score & **97.14** & 93.08 & 93.47 & 79.68 & 62.71 \\ \hline \multirow{4}{*}{26 dB} & Accuracy & **96.73** & 92.31 & 92.38 & 84.52 & 47.23 \\  & Precision & **96.07** & 93.76 & 94.33 & 85.14 & 47.65 \\  & Recall & **98.53** & 95.61 & 92.41 & 84.73 & 47.19 \\  & F1-Score & **96.7** & 93.98 & 91.84 & 84.48 & 47.37 \\ \hline \multirow{4}{*}{24 dB} & Accuracy & **94.51** & 89.3 & 92.87 & 85.18 & 41.67 \\  & Precision & **96.2** & 89.96 & 94.85 & 86.06 & 41.6 \\  & Recall & **95.69** & 88.56 & 95.7 & 85.02 & 41.57 \\  & F1-Score & **95.22** & 88.13 & 93.93 & 85.09 & 41.53 \\ \hline \multirow{4}{*}{22 dB} & Accuracy & **91.56** & 85.1 & 89.65 & 84.12 & 34.47 \\  & Precision & **93.55** & 81.03 & 84.29 & 84.72 & 34.31 \\  & Recall & **93.37** & 83.94 & 80.68 & 83.95 & 34.53 \\  & F1-Score & **92.21** & 80.44 & 80.68 & 83.86 & 34.36 \\ \hline \multirow{4}{*}{20 dB} & Accuracy & **85.23** & 76.38 & 84.51 & 83.23 & 25.55 \\  & Precision & **90.62** & 74.44 & 84.64 & 84.07 & 25.42 \\ \cline{1-1}  & Recall & **89.27** & 71 & 82.06 & 83.2 & 25.56 \\ \cline{1-1}  & F1-Score & **88.97** & 70.02 & 80.03 & 83.16 & 25.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A Summary of the evaluated indicators of different methods at various SNRs. Experimental results of our model are bolded.