# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
###### Abstract  
Deep learning technology has been widely applied in emitter identification. With the deepening research, the problem of emitter identification under the few-shots condition has become a frontier research direction. As a special communication signal, OFDM (Orthogonal Frequency Division Multiplexing) signal is of high complexity so emitter identification technology under OFDM is extremely challenging. In this paper, an emitter identification method based on contrastive learning and residual network is proposed. First, according to the particularity of OFDM, we adjust the structure of ResNet and propose a targeted data preprocessing method. Then, some data augmentation strategies are designed to construct positive samples. We conduct self-supervised pretraining to distinguish features of positive and negative samples in hidden space. Based on the pretrained feature extractor, the classifier is no longer trained from scratch. Extensive experiments have validated the effectiveness of our proposed methods. orthogonal frequency division multiplexing (OFDM); emitter identification; contrastive learning; data augmentation; ResNet +
Footnote †: journal: Medical Imaging  
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Footnote †: journal: Medical Imaging  
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Footnote 1: email: [EMAIL_ADDRESS]; Tel. be collected, which is easy to lead to overfitting and decrease the classification accuracy. Therefore, individual identification under few-shots conditions is an important challenge. As shown in Figure 1, OFDM signals carry multiple sub carriers. For our data, 108 sub carriers have high complexity and these subcarriers are transmitted in parallel, which causes the spectrum of OFDM signals to be overlapped. However, emitter identification based on convolutional neural network usually input signal's spectrum. If we input the spectrum to CNN, the model will collapse. This is the first challenge. In addition, there is often noise and mutual interference between carriers, making the signal quality poor. How to effectively learn from this kind of data is another challenge. Finally, as the sampling time is 100 ms, and most of the OFDM signals are the result of continuous sampling, which leads to very similar useful samples, and similar samples appearing repeatedly no longer distinguishable. The OFDM signal identification was studied under the condition of few-shots. As shown in Figure 2, in terms of the particularity of OFDM signals, in this paper, we propose an individual identification method of OFDM signals based on the residual network to obtain the features of amplitude and phase of two-channel data for generating. The residual network model is improved to focus on extracting the internal features of the same subcarrier at the input layer. In addition, subcarrier sequencing, time domain flip, and phase domain flip data augmentation are proposed to create positive samples, then self-supervised pretraining is carried out to distinguish learned features of positive and negative samples in hidden space to deal with the challenge of few-shots. Finally, the pretrained model parameters are used to initialize the feature extraction layer of the classifier, and the final emitter identification task is started. 4G OFDM signals in the frequency domain are collected as the dataset, and multiple groups of comparative experiments are conducted under the condition of few-shots. The results show that the proposed method to some extent solves the problem of the high complexity of OFDM signal data. At the same time, the data augmentation strategies and contrastive learning strengthens the generalization ability of the model and achieves better accuracy improvement. Figure 1: Challenges of OFDM signal identification. There are three challenges: high complexity, low quality and data redundancy. In particular, OFDM signals have 108 sub carriers, and the quality is likely to be poor, and our works are under the condition of few-shots. Our contributions are summarized as follows:  
* We constitute the first attempt to study OFDM emitter identification on deep learning methods.