## 4 Experiment and Result Analysis  
An SEI experiment was conducted on the self-acquired ESP20 dataset, and an emitter modulation method recognition experiment was carried out on the open source RML2016a dataset. The evaluation indicators of the experiment include accuracy, precision, recall, F1-score, and confusion matrix. The first experiment followed the following steps: First, noiseless raw data was used to train the ENN; second, the above evaluation indicators were used to evaluate the basic performance of the network; third, the additive white Gaussian noise was used to adjust the SNR of the data and test the robustness of the network at different SNRs. In the second experiment, as there was no noiseless raw signal, the different noise modulation signals provided in the dataset were directly used to train the ENN, which then was tested by the test set signal corresponding to the SNR. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **ESP20** & **RML2016a** \\ \hline \multirow{2}{*}{Emitter model} & \multirow{2}{*}{WIFI device signal} & Software simulation signal of \\  & & different modulation method \\ \hline Number of classes & 20 & 11 \\ \hline Number of each class & 1300 & 1000 \\ \hline Sample size & Length = 160; number of channels = 2 & Length = 128; number of channels = 2 \\ \hline Training set & 1–1000 per class is divided & 1–700 per class is divided \\ division & into training set & into training set \\ \hline Test set & 1001–1300 per class is divided & 701–1000 per class is divided \\ division & into test set & into test set \\ \hline \multirow{4}{*}{Remarks} & The 20 WIFI devices have the same & The 11 modulation methods are 8 PSK, \\  & model (ESP8266) and batch. The & AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, \\  & retained signal is the LLTF signal & PAM4, QAM16, QAM64, QPSK, and WBFM. \\  & part in the complete WIFI signal. & The B210 captures the signal and inputs it to the \\  & PC for analysis. & PC for analysis. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information summary of ESP20 dataset and RML2016a dataset. Figure 3: The production process of WIFI emitter communication platform and signal dataset. Comparison methods.To demonstrate the superiority of the method proposed in this paper, the ENN was compared with two deep learning methods and two traditional machine learning methods in the experiment. For the deep learning method, we compare our model with CNN and GCN. The structure of CNN is consistent with the network structure constituted by the sequence feature extractor and sequence classifier of the ENN, while the structure of GCN accords with the network structure constituted by the graph feature extractor and graph classifier of the ENN. Therefore, GCN and CNN are not only state-of-the-art deep-learning based classifiers, but also part of our model. The comparison with CNN and GCN is also an ablation study of the effectiveness of the ENN structure. In addition, the ENN was also compared with the traditional decision tree and k-Nearest Neighbor algorithms. Both KNN and tree are implemented using sklearn. For the KNN algorithm, we select all samples in the training set as known classes, select all samples in the test set as test samples, and set the value of K to 3. For the decision tree method, we adopted the default values from sklearn. For CNN and GCN, the number of network layers and the number of neurons use the configuration described in Section 2.2.1. All hyperparameters are obtained by cross-validation. The results showed that the method proposed in this paper has better SEI performance than traditional methods and existing deep learning methods. Training details.The ENN was trained in two steps. During training, the ENN was tested and trained on an NVIDIA GeForce GTX 950M GPU based on the PyTorch deep learning framework under Windows 10. The entire network was optimized by an ADAM optimizer with an initial learning rate of 0.001 and 50 epochs of iteration.