# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 3 Proposed work
### Proposed CNN  
Convolutional Layers: Given an input vector with \(a\) components, \(x=(x_{0},x_{1},x_{2},\ldots,x_{a-1})\)\(\in R^{a}\), the output size is the same as the input size in terms of the vector component \(y=(y_{0},y_{1},y_{2},\ldots,y_{a-1})\in R^{a}\).  
The layout of our network is depicted in Figure 3. The primary network structure is designed similarly to a VGG network. To ensure consistency among the output and input, we exclusively employ \(5\times 5\) convolutional kernels and \(2\times 2\) padding were used. We substitute the pooling layers with convolutional layers featuring enhanced stride to downsize the feature maps. Using \(2\times 2\) padding with stride of \(2\), the output is halved compared to the input layer size [31]. This method serves as a valuable alternative to maximum pooling without compromising the accuracy. In addition, we integrate projection shortcuts and dense connections to efficiently leverage information from deconstructed images. Through channel-wise concatenation, dense connections directly link each level of a deconstructed image to all subsequent layers. This linkage facilitates the seamless flow of information throughout the network. We next describe the details of two wavelet CNN applications with \(1\times 1\). The architecture of the neural network is outlined in detail in the table below, specifying the types of layers, input and output dimensions, activation functions, stride, padding, kernel size, number of kernels, and total parameters for each layer. It starts with an input layer of \(256\times 256\times 3\), representing a colored image with dimensions of \(256\times 256\) pixels and \(3\) color channels. The subsequent layers include fully connected layers, wavelet decomposition layers, Conv2D layers with ReLU activation, concatenation layers, and finally dense layers, leading to the SoftMax activated output  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Update Mechanism** & **Key Strengths / Limitations** \\ \hline \multirow{2}{*}{**QCSO**} & Quantum state update using \(\psi=\alpha|0\rangle+\beta|1\rangle\) and rotation gates & Strong global search due to quantum tunneling and superposition: robust in high-dimensional 5G feature spaces \\ \hline \multirow{2}{*}{**PSO**} & \(\
u_{i}(t+1)=w\
u_{i}(t)+c_{i}f_{1}(p_{i}-x_{i})+c_{i}f_{2}(g-x_{i})\) & Fast convergence but risk of premature stagnation in complex spaces \\ \hline \multirow{2}{*}{**CSO**} & Seeking and tracing mode based on cat behavior & Moderate performance: lacks quantum-scale diversification \\ \hline \multirow{2}{*}{**GA**} & Selection, crossover, mutation & Good exploration, but disruptive crossover may hinder exploitation \\ \hline \multirow{2}{*}{**GWO**} & \(X(t+1)=X_{p}-A\cdot D\) using alpha, beta, & Effective hierarchy but limited adaptability in dynamic 5G scenarios \\ \hline \multirow{2}{*}{**BAT**} & Frequency- and loudness-modulated echolocation & Simple and fast, but convergence stalls in high-dimensional problems \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of QCSO with Traditional Metaheuristics for Feature Extraction in 5G Wireless Systems.  
layer [32]. For instance, the first Conv2D layer (Layer 3) features a \(3\times 3\) kernel size, 64 kernels, stride of 1, and the'same' padding, resulting in an output size of \(112\times 112\times 64\). The parameter counts vary across the layers, with larger convolutional layers contributing significantly to the total number of parameters. This table offers a comprehensive overview of the network architecture, providing information about the characteristics of each layer and its role within the model [33].