# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 1 Introduction
The authors of [9] introduced Uni QGAN, a unified generative adversarial network for enhancing modulation classification, which employs multi-condition embedding and multi-domain classification techniques to handle IQ constellation diagrams under various signal-to-noise ratios simultaneously, effectively reducing training overhead and enhancing recognition accuracy at low signal-to-noise ratios. Another study [10] combined deep convolutional generative adversarial networks with conditional generative adversarial networks (CGAN) to expand the modulation signal dataset, enhancing the recognition capabilities under small sample conditions. The authors of [11] proposed a method combining numerical simulation and generative adversarial networks to input simulated samples and actual measured fault samples into GAN, generating high-quality synthetic fault samples and expanding the fault sample library for gear fault detection. The authors of [12] introduced an Adversarial Class Imbalance Learning (ACIL) method that combined CGAN with auxiliary classifiers under uneven data class distribution, using the interaction between the generator, discriminator, and auxiliary classifier to generate fault samples, enabling the model to better learn the distribution of fault samples. The authors of [13] proposed multi-module learning with gradient penalty GAN, which, through adversarial training of generators, discriminators, and classifiers, generated mechanical signals highly similar to real signals, thereby improving recognition accuracy under small sample conditions. Another group [14] proposed a filter-style GAN to separate hidden features in the feature space, reorganizing features into new signals. Although the idea of using generative adversarial networks for data augmentation has been applied in the aforementioned fields, research in the field of communication jamming signal recognition is still limited; thus, utilizing GANs to solve low recognition rates under small sample conditions in communication interference merits further exploration. To improve the recognition rate of small-sample interference signals, the following issues need to be addressed: 1. Due to the specificity of adversarial training, traditional GAN's loss function struggles to ensure training stability. 2. Traditional GANs have difficulty learning global correlation features in the time-frequency graphs of communication interference signals. 3. Time-frequency graphs contain large areas of noise information, and the ResNet network fails to focus on the key features of interference signals. The innovations of this article are summarized as follows:  
1. Design a novel adversarial loss function that combines the Wasserstein distance with a gradient penalty mechanism to alleviate the gradient vanishing issue during the training of GAN models. 2. Design an attention mechanism-embedded generative model, WDCGAN-SA, which integrates self-attention mechanisms with improved generative adversarial networks to guide the generative model to focus on the global correlation features of the jamming signal's time-frequency plots; modify the training strategy by optimizing the alternating training mode of the generator and discriminator in traditional GAN networks to multiple discriminator trainings for one generator training, improving the quality of sample generation. 3. On the basis of a deep residual network, by merging cross-channel and spatial information, the classification model focuses more on the key features of the jamming signal's time-frequency plots, enhancing recognition accuracy. The structure of this article is organized as follows: Section 2 presents the system model and data pre-processing methods; Section 3 provides a detailed introduction to the data augmentation network WDCGAN-SA; Section 4 elaborates on the classification network C-Resnet; Section 5 presents comprehensive experiments on six types of communication jamming signals; Section 6 concludes the paper.