# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 4 Proposed CCB-Guided Adaptive Channel Denoising Mechanism
### Architecture of Adaptive Channel Denoising Model  
We propose an adaptive channel dependence mechanism, which enables the end-to-end image transmission system to automatically adapt to changes in channel state without manual intervention. As the denoiser module, \(\Lambda\)CDM is designed to enhance the stability and performance of the system.  
As shown in Figure 3, \(\Lambda\)CDM consists of a UNet architecture, including the encoder1 and the corresponding decoder1, an intermediate layer, and a final output layer. The reason for using the UNet model is that the model uses skip connections to directly transfer the high-resolution feature maps extracted from each encoder layer to the corresponding decoder layer. Skip connection provides a direct path from the early layer of the network to the higher layer for high-resolution functions. When an image passes through multiple convolutional layers, it will undergo multiple transformations, which may lead to information loss. Skipping the connection helps to bypass these transformations, allowing the original high-resolution information to be retained and reintroduced into the later stages of the network. Therefore, by preserving high-frequency components and fine details, skipping the connection ensures that the denoised image retains clarity. In this way, not only is the global semantic information retained during the upsampling process, but also the rich spatial details are retained. Then, since the denoiser module only needs to recover the fading of semantic features in the channel without restoring the entire image, we reduced the number of layers in the model to two and the number of channels from 64 to 2, greatly increasing the system's operating efficiency while ensuring the completion of the task.  
In this architecture, encoder1 converts the input data \(x\) into the probability distribution parameter of the latent variable \(z\), that is,  
\[q(z|x)=\mathcal{N}(z;\mu(x),\sigma^{2}(x)), \tag{10}\]  
where \(\mu(x)\) and \(\sigma^{2}(x)\) are the mean and variance of latent variables, respectively. These parameters are generated by encoder1 to help the model sample reasonably in the data space.  
Correspondingly, decoder1 generates the distribution of the reconstructed data \(x\) through the latent variable \(z\) for  
\[p(x|z)=\mathcal{N}(x;f(z),\sigma^{2}), \tag{11}\]  
where \(f(z)\) is the mean function generated by decoder1 and \(\sigma^{2}\) is the variance. By maximizing \(p(z)\), the parameters of decoder1 can be optimized to make the reconstructed image closer to the original input.  
We adopt Kullback-Leibler (KL) divergence to measure the difference between the posterior distribution \(q(z|x)\) of the latent variable and the prior distribution \(p(z)\), with  
\[\mathcal{L}_{\text{KL}}=D_{\text{KL}}(q(z|x)\parallel p(z)), \tag{12}\]  
Figure 3: The architecture of ACDM.  
where \(D_{\text{KL}}(q(z|x)\parallel p(z))=\int q(z|x)\log\frac{q(z|x)}{p(z)}dz\). Specifically, the prior distribution \(p(z)\) is derived from the statistical characteristics of the actual data, effectively capturing the true distribution properties of the data. By leveraging this prior and the obtained posterior knowledge, the KL divergence is calculated using integral methods. This approach ensures that the model accurately reflects the underlying data distribution and optimizes its performance in various applications.