# Attention-Guided Wireless Channel Modeling and Generating
## 3 Modeling and Generating
### Se-Gan
#### 3.2.3 Feature Reweighting  
The calculated weight coefficients are multiplied back onto the original feature channels, dynamically recalibrating the characteristics of each channel's features. The Expand step extends the weight coefficients to match the spatial dimensions of the original feature map, ensuring that each channel's features are appropriately scaled by the corresponding weight coefficient. Once the weight coefficients (attention scores) have been computed, the final crucial step is to re-weight the original feature map by multiplying it with the attention values. The output tensor y from the fully connected layers has a shape. To apply these attention weights across the spatial dimensions of the input feature map, we use the Expand method to match the original size of the input feature map. This step ensures that each channel in the input feature map is multiplied by its corresponding weight across all the spatial locations. The resulting tensor is then returned as the output of the SEL, with the feature map dynamically adjusted according to the learned attention weights. This adjustment allows the model to focus on more important features and suppress less relevant ones, ultimately improving the model's performance by enhancing the representation of important channels. The SE-GAN structure is depicted in Figure 4. The generator is a key component of the SE-GAN, specifically designed for channel modeling and generation tasks. The generator is responsible for transforming a latent vector, which typically represents a random noise or encoded information, into a structured output that simulates the distribution of real data. The generator is composed of linear blocks and transposed convolution blocks, incorporating a channel attention mechanism SEL. The primary function of the linear block is to map the latent vector into a high-dimensional feature space. The linear block serves as the initial stage of the generator, where the latent vector is processed to form an initial feature map that will later be up-sampled through transposed convolutions. Specifically, a fully connected layer maps the input latent vector to a feature vector of a designated dimension, helping the model learn richer feature representations. This is followed by the batch normalization of the feature vector to accelerate the training process and stabilize model training. Subsequently, a Leaky ReLU activation function introduces a nonlinear transformation, enhancing the model's expressive capability. This activation function enhances the model's ability to capture nonlinear relationships in the data. The transposed convolution block comprises five transposed convolution layers, each followed by a batch normalization layer, an SEL, and a Leaky ReLU activation function to adaptively recalibrate the importance of different channels during the up-sampling process. The SEL is crucial for generating high-quality outputs that capture detailed and meaningful patterns. The fifth transposed convolution layer up-samples the feature map to a single channel, and the generated channel data are output through a Tanh activation function, which normalizes the values to the range of \([-1,1]\). This is a common practice in channel modeling tasks to ensure that the generated data values fall within the correct range. The discriminator is another key component of the SE-GAN. The discriminator's primary responsibility is to distinguish between real and generated data by learning to assign a probability score that indicates whether input data are real or fake. The discriminator achieves this through a series of convolutional layers followed by fully connected layers, enabling it to effectively capture local patterns and spatial features in the input data. The discriminator consists of three main modules: a zero-padding module, a convolution block,and a linear block. The zero-padding module preprocesses the input signal through a zero-padding function.