# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 6 Conclusions  
Addressing the issue of low recognition rates due to the difficulty in obtaining communication jamming signal data samples, this paper proposes a WDCGAN-SA model embedded with an attention mechanism to augment and expand the time-frequency plots of communication jamming signals. Subsequently, a classification model based on C-ResNet was constructed to achieve recognition of communication jamming signals under small sample conditions. The experimental results indicate:  
1. The WDCGAN-SA generative model addresses the problem of training gradient vanishing caused by the original GAN model's use of JS divergence to measure the distribution distance between real and generated data by introducing the Wasserstein distance and gradient penalty term. Additionally, by incorporating attention modules into the generative model, the ability to learn signal features from the time-frequency plots of communication jamming signals is enhanced. As a result, experiment 5.2 demonstrates that, compared to the traditional DCGAN network, the WDCGAN-SA model significantly improves the quality of generated samples.
2. To enhance the feature extraction capability of the classification model and thereby improve recognition accuracy, this paper integrates the Convolutional Block Attention Module (CBAM) into a deep residual network. CBAM extracts features by combining  
Figure 10: The recognition rates of four different generative networks were evaluated under various conditions of sample size enhancement. (**a**) Enhancement with 30 images; (**b**) enhancement with 60 images; (**c**) enhancement with 90 images.  
spatial and channel information, allowing for a greater focus on key features in the time-frequency plots of communication jamming signals, thereby improving the classification accuracy of the residual network. Experiment 5.3 shows that the recognition rate of the C-ResNet classification model is 4.23% higher than that of the traditional CNN model.
* Experiments 5.4 and 5.5 indicate that compared to traditional models such as GAN, WGAN, and DCGAN, the proposed WDCGAN-SA-based method for recognizing communication jamming signals with small samples achieves higher recognition rates across datasets with varying amounts of time-frequency plots. This method meets the requirements for accurate classification under conditions of scarce communication jamming signal data. Additionally, the experiments conducted in this study validate the effectiveness of the proposed method, providing theoretical support and insights for the recognition of communication jamming signals with small samples.  
Conceptualization, R.G., Y.L. and Y.Z.; methodology, R.G., Y.L. and Y.Z.; software, R.G. and M.C.; validation, R.G., X.Z. and K.Z.; formal analysis, R.G., Y.L. and Y.Z.; investigation, R.G., X.Z. and K.Z.; resources, R.G.; data curation, R.G.; writing--original draft preparation, R.G.; writing--review and editing, R.G., Y.L. and Y.Z.; visualization, R.G.; supervision, R.G.; project administration, R.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are not available due to privacy.  
The authors declare no conflicts of interest.