# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
## 2 Related Work  
Emitter Identification. The key of individual identification is to extract effective features of emitter resources. In the complex electromagnetic environment, individual features are difficult to extract accurately. The authors of [2] proposed an individual identification method based on wavelet transform for feature extraction. Ref. [3] realized the identification of speech signals by calculating time-frequency singular values and singular vectors and adopting a Gaussian classifier. [PERSON] et al. [4] used the Babbitan distance to select the bispectrum and improve the rectangular integral bispectrum to obtain features. The authors of [5] proposed a global low-rank representation to extract subtle features. The authors of [6] proposed extraction of signal integral bispectrum as the feature vector. [PERSON] et al. [7] proposed a method based on nonlinear dynamical features. Some achievements have been made in the classification of communication radiation sources by feature extraction, but such methods often need more manual pretreatment and a large number of experiments to verify the effectiveness of the feature extraction method, which is difficult to be applied in practice for different electromagnetic signals. Neural Network models. With the development and wide application of deep learning, more and more experts and scholars apply the method of deep learning on identification problem, such as human intention recognition [8; 9], video semantic recognition [10] and emitter identification [11; 12; 13; 14] The residual neural network proposed by [15] solved the performance degradation caused by increasing the depth of CNN through the residual module. [PERSON] [16] optimized the deep learning model by combining LSTM and integrating features extracted from different convolutional layers. The authors of [17] proposed  
Figure 2: Overall structure of OFDM signal classification. There are four parts in our structure, the data processing module will generate two-channel datasets, and the data augmentation module will generate positive samples for constrastive learning. Then, we will pretrain the contrastive learning module and initialize the classification module with pretrained model’s parameters. a radiation source individual identification algorithm based on the stack-based LSTM network, which directly used IQ time series signals to train the LSTM network. The authors of [18] converted IQ road data into images and processed the images with the convolutional neural network to complete the classification and identification task. The authors of [19] proposed a method for individual identification of radiation sources based on the deep confidence network under the condition of small samples. The authors of [20] applied convolutional neural network in radio cognition technology. [PERSON] et al. [21] proposed an identification method based on the deep residual adaptation network to realize migration identification from the source domain to the target domain. The authors of [22] inputs i-channel and Q-channel signals into the deep convolutional neural network and achieves good results in radio classification tasks under different modulation modes. Contrastive learning. Contrastive learning has gradually become a mainstream visual self-supervision task. Many works [23; 24; 25; 26; 27] construct positive sample pairs by random data augmentation operations on images, decrease the feature distances of different forms of the same image, and increase the feature distances between different images. The authors of [28] presented Contrastive Predictive Coding (CPC) for extracting compact latent representations to encode predictions over future observations. Pretraining with contrastive learning is dedicated to learning generic representations, based on the intrinsic adequacy of the data itself, making great use of existing data for knowledge learning, and can be easily migrated to downstream tasks.