# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 4 Results and Discussion
Starting from an initial accuracy level of 2.94% at 10 iterations, the proposed network steadily improved and achieved 97.78% accuracy at 200 iterations. In contrast, the other architectures showed varying performance, with some exhibiting notable enhancements, while others reached plateaus or showed fluctuations. The proposed wavelet network achieved high training accuracy throughout the iterations, indicating its potential as a robust and efficient neural network architecture for spectrogram images. During the initial iterations (from 0 to 50), the training loss of the proposed wavelet network decreased more rapidly compared to that of most baseline architectures, indicating its learning efficiency in the initial training phases. As training advanced (iterations 50 to 150), the proposed wavelet network consistently recorded lower training losses than the other networks, suggesting its potential to capture patterns and features in the training data. In subsequent iterations (beyond 150), the training loss for all networks, including the proposed wavelet network, appeared to converge, as depicted in Figure 6. This convergence is a typical occurrence as a model adjusts its parameters and the pace of improvement declines. In particular, the proposed wavelet network achieved the lowest training loss in the later iterations, signaling its ability to capture intricate patterns in the data. It should be noted that a lower training loss does not imply superior performance on unseen data (validation or test set). The evaluation of models using different datasets is crucial to determine their generalizability. Furthermore, the consideration of additional metrics such as the validation loss and accuracy would enable a more comprehensive assessment of the model's performance. Figure 6: (a). Training accuracy for all methods, (b). Training loss graphs for all methods. The 5G spectrogram signals were then analyzed, and the resulting spectrograms are depicted in Figures 7 and 8. Figure 9 presents a comparison of various deep learning models, such as ResNet18, ResNet50, ImageNet, GoogleNet, AlexNet, and ConvNet, based on their sensitivity values. Sensitivity, also referred to as recall, reflects a model's ability to accurately detect positive instances among the total number of actual positives. Models with higher sensitivity values excel in capturing true positive cases. WaveletNet stands out, with a sensitivity value of 0.999, indicating its exceptional performance in correctly identifying positive instances. ResNet50 and ImageNet also demonstrate high sensitivity values of 0.980 and 0.983, respectively, highlighting their effectiveness in capturing true positive cases. These sensitivity results offer insights into the models' ability to accurately identify relevant features and generate correct predictions. WaveletNet displays particularly strong performance in this regard. Meanwhile, the specificity values for ResNet18, ResNet50, ImageNet, GoogleNet, AlexNet, and ConvNet remain consistently high at 0.999, demonstrating their ability to correctly identify true negative instances. We also note WaveletNet's superior performance, with a specificity of 1.000, indicating perfect accuracy in detecting true negatives. This comparison indicates that WaveletNet outperforms the other models in discerning negative instances, highlighting the value of integrating wavelet transforms in image classification tasks. Precision is a metric that reflects the accuracy of the positive predictions generated by a model. Higher precision values indicate fewer false positives. Here, WaveletNet shows the highest precision among the listed models at 0.990, suggesting superior performance in identifying positive instances compared to established architectures such as ResNet18, ResNet50, ImageNet, GoogleNet, AlexNet, and ConvNet, as depicted in Figure 9. Deep learning models also display strong performance, particularly accuracy, after a set number of iterations.