# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 4 Results and Discussion  
The 5G dataset includes signals, each converted into a spectrogram representation for feature extraction. The spectrograms were generated using short-time Fourier transform (STFT) to capture the time-frequency characteristics of the signals effectively. Regarding the training-validation-test split, we acknowledge the standard 80-10-10 split used in the MathWorks example. However, in our study, we adopted a custom split ratio of 60-20-20, optimized for our specific feature selection and classification task. This decision was based on an extensive evaluation of model generalization performance and computational efficiency. The dataset and processing methodology are clearly documented to ensure reproducibility. In this work, QCSO optimization algorithm is employed to optimize important feature selection in 5G signals. The input parameters for QCSO are as follows: the input population swarm size is 50, the number of iterations is 100, the initial SMP is 0.63, the initial tracking mode probability is 0.37, and the rotation angle coefficient is 0.04. Here, we initialized the values of all parameters in QCSO as follows: SPC: 0.5, SRD: 0.231, CDC: 3, maximum velocity limit: \(V_{max}=2.0\), acceleration coefficients: \(c_{1}=2.0\), \(c_{2}=2.0\), these values are varied in each iteration. These values ensure efficient exploration and exploitation in the feature selection process [29]. The rotation angle coefficient controls the step size for updating quantum states in the rotation gate transformation. It ensures that qubits adjust efficiently without excessive oscillation. The tracking mode probability determines the proportion of cats that operate in tracing mode rather than seeking mode, balancing between global exploration and local exploitation for optimal feature selection. For the WCNN to detect the 5G signals based on spectrogram, the input image size is \(256\times 256\times 3\), representing RGB spectrograms of the signals. Input dataset is split into 60:20:20 ratios, which means 60% of dataset used for training, 20% is used for validation and remaining 20% is used for testing. The learning rate parameter is set to 0.01 and the WCNN model is trained for 200 epochs with a batch size of 16 using the Adam optimizer. These parameters are optimized to enhance the classification accuracy of 5G signal detection. As shown in Table 3, QCSO achieves the fastest convergence speed (450 iterations), significantly outperforming traditional methods such as the genetic algorithm (GA) (1500) and DE (1200). It also exhibits a well-balanced exploration-exploitation strategy (0.9), ensuring strong global  
Figure 3: Proposed wavelet CNN-based 5G signal identification method. and local search capabilities. The precision in feature selection (95.3%) is the highest, demonstrating its effectiveness in identifying relevant features. In terms of computational efficiency, QCSO exhibits the shortest execution time (2.9 s), matching that of gray wolf optimization (GWO), while it is considerably faster than GA (5.2 s) and DE (4.7 s). Furthermore, its robustness (96.8%) ensures consistency across multiple runs, reducing the likelihood of unstable results. QCSO also excels in terms of signal reconstruction efficiency (24.1 dB SNR) and energy efficiency (0.88 J per operation), making it the optimal choice for real-time 5G applications. Although Harris Hawks optimization (HHO) and GWO exhibit competitive performance, QCSO consistently outperforms them in several critical parameters. In general, the results confirm that QCSO is the most effective approach, providing the best trade-off between accuracy, speed, robustness, and computational efficiency and optimizing the extraction of 5G signal features. Among the eight comparative parameters, QCSO shows a consistent improvement in seven. The only exception is the _Avoidance of Local Minima_, where QCSO achieves 83.2%, slightly lower than HHO's 88.4%. This minor shortfall is due to the enhanced exploratory mechanisms of HHO driven by stochastic hawk behavior.