# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 5 Simulation and Analysis
### Comparative Experiments of Different Classification Networks  
To verify the performance of the classifier C-ResNet proposed in this paper, combination experiments were conducted with the WDCGAN-SA enhancement network and different classifiers. The classifiers include the C-ResNet proposed in this paper, Convolutional Neural Network (CNN), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). For each category, 98 real samples and 90 generated samples were mixed and used as the training set, which was then input into the aforementioned five classification networks for comparative experiments. Figure 7 presents the results of the combination experiments between the WDCGAN-SA enhancement network and the five classifiers.  
Figure 6: Comparative of Real Samples and Samples Generated by Two Different Network Models.  
Figure 7: The recognition accuracies of five different classification networks.  
Analysis of the results depicted in Figure 7 reveals that at a JNR of \(-10\) dB, the noise frequency in the time-frequency plots of the jamming signals is relatively high, resulting in more obscure signal characteristics. The recognition rates of several classification networks are low, ranging between 20% and 30%. Due to the incorporation of a spatial channel attention mechanism in the C-Resnet classification, which aids the model in focusing on important features, the recognition rate of the C-Resnet network at \(-10\) dB is 34.01%, slightly higher than that of other networks. As the JNR increases, the clarity of the signal features improves, and the recognition rates of all networks gradually increase. Notably, at a JNR of 10 dB, the recognition rate of the proposed model is 96.01%, while the accuracy rates for traditional algorithms like KNN, SVM, and RF are 75.13%, 79.71%, and 71.22%, respectively. This discrepancy is primarily due to traditional classification networks being limited by manually extracted features, which result in lower recognition rates compared to deep neural networks. The overall recognition accuracy of the proposed network from \(-10\) dB to 10 dB is higher than that of the CNN classification network.  
Furthermore, at a JNR of 10 dB, the recognition accuracy of the proposed network is 96.01%, which is 4.23% higher than CNN's 91.78%. Figure 8a,b are the confusion matrices for the CNN classifier and the C-ResNet classifier at a JNR of 10 dB, respectively. The confusion matrix indicates that the CNN classifier often misclassifies narrowband jamming as single-tone jamming and comb frequency jamming as multi-tone jamming. This misclassification occurs because both narrowband jamming and single-tone jamming have a single spectral line, while multi-tone jamming and comb frequency jamming have multiple spectral lines, leading to classification errors. In contrast, the C-ResNet classifier significantly reduces these misjudgments. In summary, under both low and high JNR conditions, the C-ResNet classification network demonstrates a higher recognition rate compared to traditional CNNs and other conventional classification networks.