# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Results Analysis
#### 5.5.4 Robustness Performance  
Due to the complex satellite-ground transmission environment, it is impossible to take all channel conditions into account during model training. Therefore, it is necessary to test the robustness of the model to ensure that the proposed model can still work stably in non-training scenarios.  
Figure 7 illustrates the performance of the proposed CCB model under different testing and training environments. The training scenario involves a light fading channel, while the test scenario involves a heavy fading channel. The diagram indicates that the JSCC model, which employs a CNN mechanism, has a relatively simple architecture and thus relies heavily on the training environment. Consequently, when the test environment differs, its PSNR falls significantly below 10 dB, and its SSIM drops below 0.2, rendering image transmission nearly impossible. The introduction of a pilot mechanism in our proposed CCB model allows it to select model parameters that are most closely aligned with the actual test scenario. As a result, it can improve by more than 2 dB compared to the WITT  
Figure 5: (**a**) SSIM performance over the light fading channel. (**b**) SSIM performance over the heavy fading channel. (**c**) SSIM performance over the average fading channel.  
model at low SNR. These results demonstrate that the CCB model performs well, even in the absence of corresponding parameters for the current test environment.  
Figure 8 illustrates the performance of the proposed CCB model under different training and test datasets, simulating various satellite transmission image tasks. The training dataset used is DIV2K, while the test dataset is CUB-200-2011 [45]. The graph shows that the performance of the proposed CCB model is comparable to the WITT model, with a PSNR exceeding 25 dB. This is because, in high-SNR environments, noise interference on image restoration is minimal. As a result, the quality of image restoration predominantly relies on the inherent capabilities of the encoder and decoder. Both CCB and WITT models  
Figure 6: The first two lines are visual comparison examples under light fading with SNR = 20 dB. The last two lines are visual comparison examples under light fading with SNR = 5 dB. The first column to the fourth column show the original image and the reconstructed image under different transmission schemes.  
Figure 7: (**a**) PSNR performance under different training scenarios and test scenarios. (**b**) SSIM performance under different training scenarios and test scenarios.  
utilize an architecture based on the swin transformer, which captures more semantic information. In contrast, the Deep JSCC model, employing a simpler CNN architecture, heavily relies on the features within the training dataset. When there is a significant disparity between the test and training datasets, the JSCC model struggles to adapt to new data features, resulting in a substantial performance drop. Consequently, the SSIM of the JSCC model in the new test dataset falls below 0.2, rendering it practically unusable.  
These results highlight the superior adaptability and robustness of the proposed CCB model in handling diverse and complex test scenarios compared to other models.