# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 1 Introduction  
Due to the openness of wireless channels, information transmission via wireless communication devices is highly susceptible to attacks by noise or deliberate malicious jamming. Typical patterns of malicious jamming include single-tone, multi-tone, narrowband, and linear sweeping frequency--all of which can significantly reduce the security and reliability of communication transmissions. Therefore, identifying such malicious jamming provides prior knowledge for subsequent anti-jamming decision-making and the selection of anti-jamming waveforms, thereby ensuring the reliability and security of communication transmissions. This is of great importance for communication anti-jamming efforts [1]. In recent years, deep learning has made extraordinary progress in many fields, including signal recognition, due to its exceptional data processing capabilities. Reference [2] utilizes an improved deep convolutional neural network (CNN) to automatically extract features of modulation signals from a large amount of training data, accomplishing modulation signal classification in complex communication channels. Reference [3] uses a deep, sparse denoising autoencoder to extract features for suppressing interference in satellite navigation systems. A total of 60,000 labeled signal samples are used for training, and the extracted features are integrated into a stacked model composed of support vector machines and random forests to classify interference signals. When the signal-to-noise ratio is greater than \(11\) dB, the recognition rate reaches \(100\%\). One study [4] used Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Thresholding (WT) methods to perform three-stage decomposition denoising on the original radar signaland converted the processed signal into a time-frequency image through Choi Williams Distribution (CWD). Then, a neural network equipped with dilated convolution learned from a large number of time-frequency images and achieved a recognition rate of 75.3% under low SNR conditions. Another study [5] proposed a WIR converter consisting of a Local Attention Fusion Layer (LAFL) and a Patch Aggregation Module (PAM). This model applies Transformer technology to the field of wireless signal recognition (WIR) and uses a region partitioning strategy to independently calculate self-attention within each partitioned region, thereby reducing computational complexity. After extensive sample training, this method has higher recognition accuracy compared to traditional wireless signal recognition techniques. Deep learning-based signal recognition methods typically require a large number of training samples to uncover deep data features. However, in complex electromagnetic environments, the quantity of communication jamming signal samples available for training is often limited. When the number of samples is insufficient, the capability of deep learning to automatically extract and learn features significantly decreases, leading to severe degradation in recognition performance. Therefore, accurately recognizing communication jamming signals under small sample conditions is critical. The introduction of Generative Adversarial Networks (GAN) [6] has provided a new direction for addressing small sample problems, and researchers have already applied GANs to modulation signal recognition, fault diagnosis, and other fields. The authors of [7] proposed a GAN-based joint data augmentation method that uses a similarity principle to establish a filtering mechanism, obtaining high-quality generated signals, and inputting the augmented dataset into a designed CNN network to accomplish classification and recognition, achieving good electromagnetic signal classification performance under small sample dataset conditions. The authors of [8] designed a semi-supervised learning framework based on GAN that directly processed IQ signal data and fully utilized unlabeled samples to achieve the end-to-end precise classification of a small number of electromagnetic signals.