## 5 Conclusions  
In view that the signals used in some existing studies were sourced from simulation software and the dataset was less representative in practice, the authors designed a WIFI emitter communication platform, acquired WIFI signals practically, and formulated the dataset. To fully extract fingerprint features from a signal, a signal graph was designed and used to have the signal sequence transformed from a Euclidean space to a non-Euclidean space; furthermore, an ensemble neural network (ENN) was designed, making it available to extract features from the signal sequence and signal graph simultaneously. Subsequently, the extracted sequence and graph features were fused together, forming an ensemble feature with richer information than a single sequence or graph feature. Finally, the ENN was trained in two steps. In the experiments, there were four comparison methods, including two traditional SEI methods (decision tree and k-nearest neighbor algorithms) and two deep learning SEI methods (CNN and GCN). Among them, CNN has the same network structure as the network structure formed by the sequence feature extractor and sequence classifier of the ENN, while GCN has a completely identical network structure to the network structure formed by the graph feature extractor and graph classifier of the ENN. Therefore, the experiment on CNN and GCN is an ablation study of the network modules of ENN, which can indicate the effectiveness of the modules. In the experiments, SEI was conducted on the ESP20 dataset, and emitter modulation method recognition was carried out on the open-source RML2016a dataset. The experimental results demonstrated that the proposed method performed the best in accuracy, precision, recall, and F1-score at various SNRs. This indicates that this method can effectively improve the SEI's performance on the basis of CNN and GCN. Furthermore, the best performance of the proposed method at various SNRs proves that this method has the highest robustness, and the optimum performance of the method in different SEIs reveals that our method has high generalization.  
Methodology, C.X.; Writing--original draft, Y.P.; Writing--review & editing, Y.Z., J.H. and S.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (62171320 and U2006211) and the National Key Research and Development Program of China (2020 YFC1523200).  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors acknowledge the financial supports of the National Natural Science Foundation of China (62171320 and U2006211) and the National Key Research and Development Program of China (2020 YFC1523200).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & **Index** & **ENN** & **CNN** & **GCN** & **KNN** & **TREE** \\ \hline \multirow{4}{*}{\(-2\) dB} & Accuracy & **64.09** & 55.55 & 60.64 & 9.33 & 15.48 \\  & Precision & **64.93** & 56 & 60.41 & 15.42 & 15.65 \\  & Recall & **64.68** & 55.57 & 60.59 & 9.17 & 15.55 \\  & F1-Score & **64.67** & 55.69 & 60.47 & 3.23 & 15.57 \\ \hline \multirow{4}{*}{\(-4\) dB} & Accuracy & **53.73** & 48.91 & 53 & 9.18 & 13.91 \\  & Precision & 53.07 & 48.78 & **53.1** & 10.09 & 14 \\  & Recall & 52.66 & 48.81 & **53.01** & 9.09 & 13.85 \\  & F1-Score & 52.84 & 48.77 & **53.04** & 1.82 & 13.88 \\ \hline \multirow{4}{*}{\(-6\) dB} & Accuracy & **42.55** & 40.55 & 39 & 8.7 & 11.45 \\  & Precision & **42.91** & 40.67 & 38.37 & 8.09 & 11.47 \\ \cline{1-1}  & Recall & **42.94** & 40.27 & 38.68 & 9.09 & 11.56 \\ \cline{1-1}  & F1-Score & **42.84** & 40.33 & 38.47 & 1.48 & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._