# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
## 3 Proposed Method
### Contrastive Learning and Residual Network Framework
The experimental results are shown in the \"Input\" part of Table 1. Taking the spectrum map as the input, the performance is poor, the reason is OFDM contains 108 subcarriers, the overlap between subcarriers will blur the features, and the information utilization is very low. In contrast, by using origin data to make the channel dataset, more information is retained to achieve higher accuracy. The complex number can be described from the two dimensions of amplitude and phase, and the dual-channel data made by using both amplitude and phase achieves the highest accuracy. ResNet structural adjustment experiment analysis. As mentioned in Section 3.4, in view of the particularity that OFDM signals contain multiple subcarriers, we adjust the parameters of the ResNet input layer. Here, we compared the adjusted residual network structure with ResNet18, and the experimental results are shown in the \"Model\" part of Table 2, after adjusting the structure of the input layer, the 1 dResNet18 will focus on the features of a subcarrier rather than between the subcarriers, and all subcarriers will be considered to make the information utilization more reasonable and sufficient. Therefore, the average accuracy is 2.58% higher than ResNet18, and the standard deviation is also smaller, which proves the effectiveness of this method. \begin{table}
\begin{tabular}{c c c} \hline
**Input Dim** & **Flops** & **Parameters** \\ \hline (1, 2, 108, 1200) & 9828 M & 11.17 M \\ (8, 2, 108, 1200) & 78,624 M & 11.17 M \\ (16, 2, 108, 1200) & 157,248 M & 11.17 M \\ (32, 2, 108, 1200) & 314,497 M & 11.17 M \\ \hline \end{tabular}
\end{table} 
Table 1: Flops and parameters of our model. Batch-size will affect flops while parameters are fixed. The input dim is (batch-size, channel num, dim1, dim2). Pretraining based on contrastive learning experiment analysis. On the basis of our adjusted residual network, we introduce the work of pretraining based on comparative learning. During pretraining, we will build positive samples through the data augmentation method, and other samples in the same batch will be used as negative samples for training through InfoNCE loss. We have three data augmentation methods in total. At the same time, we conduct the experiment of selecting a data augmentation method randomly in each training iteration. As shown in the \"Data augmentation in contrastive learning\" of Table 1, we proposed three data augmentation methods to generate positive samples for self-supervised pretraining. 1 dResNet18 without pretraining was used as baseline (70.86%). It is clear that all three methods achieve better results. The highest average accuracy of 80.03% was achieved when phase domain flip. However, although the maximum accuracy of the time domain flip method is lower, its average accuracy is higher than that of subcarriers rerank, and it has a smaller standard deviation, indicating that it is more stable. In addition, we conducted a set of experiments using random data augmentation methods, each time randomly using one data augmentation method, and obtained the highest average accuracy of 81.55%. Additional experiments. In order to better show the difficulty of OFDM signal identification task, we conducted an additional experiment, we collected three categories of signals from radio signals, which have no subcarriers, and trained them on adjusted ResNet. As shown in the \"Radio signals\" part of Table 2, the radio signal dataset achieved a good performance on ResNet, and the average accuracy rate is very high and stable. Such results are good enough to support practical applications, which reflects the powerful feature extraction and classification ability of ResNet, and also reflects the difficulty of our task. ### Model Robustness Experiment

We conducted a group of experiments to verify the robustness of our method on radar signals. First, divide the whole dataset into two parts; the training data set is of \(10^{5}\) magnitudes. We use ResNet as a baseline.