## 3 Proposed Design
### Squeeze-Excitation Network  
Convolutional filters are responsible for extracting low- and high-level features from the input data. As the data proceed to the successive layers, the spatial and channel information of the data are fused together to retain the best representation of the input. In conventional CNNs, each input channel is specifically weighted equally to create an output activation for the next layer. In SENets, however, each channel-wise feature value is adaptively readjusted by explicitly modeling interconnection between channels. This is carried out by squeezing the input feature maps to a single number using global average pooling, resulting in a vector of length \(C\), where \(C\) is the number of convolutional channels. The input feature map of the squeeze-and-excitation block is denoted by \(X\in\mathbb{R}^{H\times W\times C}\)  
Figure 2: Residual block. where \(H\), \(W\), and \(C\) represent the height, width, and number of channels of the input feature map, respectively, and are mathematically represented as  
\[z=\text{GlobalAvgPool}(X)\in\mathbb{R}^{C} \tag{6}\]  
Here, the function GlobalAvgPool performs the average pooling operation over the height and width dimensions of the input feature \(X\). Moreover, to perform the excitation operation, the vector is fed to a network consisting of two fully connected layers, which output a vector with the same length as the input. The weight matrices of these two FC layers are denoted as \(W1\) and \(W2\), which can be expressed as  
\[W_{1}\in\mathbb{R}^{\frac{C}{\tau}\times C}\;\;and\;\;W_{2}\in\mathbb{R}^{C \times\frac{C}{\tau}} \tag{7}\]  
These \(C\) values are then used as weights on the original feature maps with each channel scaled according to their importance. The authors of SENet in [47] demonstrated that adding the SE blocks in the ResNet-50 results in a similar accuracy to that of ResNet-101, requiring only half of the computational cost. The design of the proposed SE block is shown in Figure 3. As represented, the input has two connections, one of which leads directly to the output, and the other of which is fed through an average pooling, in which each channel is squeezed into a numeric value. Furthermore, it is followed by a dense layer that contains a leaky ReLU, which assists in reducing the output channel complexity by the reduction ratio \(r\), which is set to 16, such that the intermediate output of the first FC layer has a smaller dimension. The output of the excitation process can be written as follows, where \(\sigma\) is the softmax function that gives each channel a smooth gating function. \[s=\sigma(W_{2}\cdot\text{LeakyReLU}(W_{1}\cdot z))\in\mathbb{R}^{C} \tag{8}\]  
Here, LeakyReLU denotes the leaky rectified linear unit activation function. Finally, the output of the excitation operation is reshaped to have the same size as the input feature map \(X\), and multiplied element-wise with the input feature map \(X\) to produce the output of the squeeze-and-excitation block; illustrated as  
\[Y=\text{reshape}(s)\cdot X\in\mathbb{R}^{H\times W\times C} \tag{9}\]  
In Equation (9), the function reshape(.) transforms the input feature map \(s\) to have the same shape as the input feature map \(X\). The skip connection from the input feature map \(X\) to the output feature map \(Y\) is added element-wise after the reshaping and scaling operations to obtain the final output of the block. Overall Proposed Block  
In various advance communication systems, accuracy is among the most significant variables affecting the quality of service. This implies a well-performing AMC model on the basis of computational power and classification performance, specifically if AMC is  
Figure 3: Proposed squeeze block. designed for application on edge devices for the end users. Therefore, this section presents the design of the proposed cost-effective method for modulation classification, utilizing the residual learning and squeeze-and-excitation networks. Figure 4 depicts the architecture of the proposed modulation classification technique.