# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
## 3 Proposed Method
### Contrastive Learning and Residual Network Framework
Randomly select \(10^{2}\)-level data in the training data set, use the Contrastive Learning framework to process the data, then use the same algorithm to test. Results are shown in the following Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Max Accuracy** & **Average \(\pm\) Deviation** \\ \hline Input & spectrum map & 32.26 & 32.21 \(\pm\) 2.16 \\ Backbone:ResNet18 & amplitude and phase & 70.69 & 67.07 \(\pm\) 3.65 \\  & amplitude and phase & 72.41 & 68.28 \(\pm\) 2.80 \\ \hline Model & 1 dResNet18 & 74.14 & 70.86 \(\pm\) 1.43 \\ \hline \multirow{4}{*}{Data augmentation in Contrastive learning} & Subcarrier rerank & 82.76 & 78.45 \(\pm\) 2.07 \\  & Time domain flip & 81.03 & 79.82 \(\pm\) 1.10 \\ \cline{1-1}  & Phase domain flip & 82.76 & 80.03 \(\pm\) 1.50 \\ \cline{1-1}  & Random & 84.48 & 81.55 \(\pm\) 1.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: OFDM signals and radio signals identification max accuracy and average accuracy. We show the effectiveness of our proposed method by gradually adding proposed modules. Radio signals is different from OFDM signals, which has only one carriers without knowing the modulation type. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training Set Size** & **Accuracy** & **Gain** \\ \hline ResNet & 50,000 & 96.86\% & - \\ ResNet & 500 & 85.98\% & - \\ Ours & 50,000 & 97.48\% & +0.62\% \\ Ours & 500 & 90.40\% & +4.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model accuracy when the number of training set samples drops sharply. According to the experimental requirements, when the training set is of \(10^{5}\) magnitudes, baseline achieves an accuracy of 96.86%, while when the training set is reduced to \(10^{2}\), the baseline accuracy is reduced to 85.98%. As for our proposed framework. When the training set is of \(10^{5}\) magnitudes, our framework achieves the highest recognition accuracy of 97.48%. The experimental result is 0.62% higher than the baseline. When the training set size is reduced to \(10^{2}\), our method can achieve the highest recognition accuracy of 90.40%. The experimental result is 4.42% higher than the baseline. It has achieved more stable results, which represents that our method has better robustness. ## 5 Discussion

There are few works on emitter identification in OFDM. We proposed methods from data collection to classification. One limitation of this work is that the identification accuracy is not enough to support the practical application. In the above discussion, we have confirmed the difficulty of this task and the difference between it and the general emitter identification tasks. We expect that our work can motivate more researchers to explore this research direction, and finally, practical application performance can be achieved. ## 6 Conclusions

Orthogonal frequency division multiplexing (OFDM) signals are widely used in daily life, and the task of emitter identification on OFDM is extremely challenging. In this paper, we proposed a framework based on contrast learning and residual networks. In view of the feature that it contains multiple subcarriers, dual channel input and 1 dResNet are used to extract better features from complex signals and make full use of data. Three data augmentation strategies are proposed to construct positive samples and achieved good performance under the condition of few-shots. Extensive experimental results demonstrated the effectiveness of our method. Conceptualization, J.Y. ; data curation, Q.Z. and W.Z. ; formal analysis, Y.Y. ; funding acquisition, Q.Z. and W.Z. ; investigation, J.Y. ; methodology, J.Y., Y.Y. and Z.F. ; project administration, J.Y. ; resources, F.J., Q.Z. and W.Z. ; software, Q.Z. and W.Z. ; validation, J.Y. and Z.F. ; visualization, J.Y. ; writing--original draft, J.Y. ; writing--review and editing, J.Y., F.J. and Y.Y. All authors have read and agreed to the published version of the manuscript.