# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 6 Conclusions  
Satellite-ground communication faces significant challenges in data transmission efficiency due to the dynamic nature of satellite communication channels. In this paper, we have presented a CCB architecture for wireless image transmission in satellite-ground scenarios, which demonstrates the ability to flexibly adapt to different channel states in complex channel environments. Firstly, we construct a well-designed CCB framework based on the emerging swin transformer backbone network, which outperforms the traditional CNN-based neural JSCC. Then, we design a MappingNet to map the environmental parameters to the corresponding decoding parameters. Through joint training under different satellite-ground transmission channels, we construct a CCB-based denoiser parameter library and determine the model parameters of the denoiser by sending specific pilots, so as to realize adaptive adjustment of different channels and noises without changing the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **CCB** & **WITT** & **Deep JSCC** \\ \hline Inference Time (s) & 3.7689 & 4.2914 & 2.8360 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model inference time.  
Figure 8: (**a**) PSNR performance under different training dataset and test dataset. (**b**) SSIM performance under different training dataset and test dataset.  
state of the encoder. The experimental results show that, compared to the CNN-based JSCC system and the ordinary swin transformer system WITT, our CCB model can achieve better performance, adapt to more channel environments, and have stronger robustness, especially under high SNR conditions.  
In future work, to better adapt to real satellite-ground image communication scenarios, we plan to incorporate a broader range of channel environments for training and testing. This will include the use of a frequency-selective fading tapped delay line (TDL) channel model and Doppler frequency-shift satellite channels. By diversifying the channel conditions, we aim to enhance the robustness and effectiveness of our communication model across different real-world environments.In addition, to more accurately evaluate the performance of image transmission and semantic loss in downstream tasks (such as object detection or classification), we plan to adopt more comprehensive evaluation metrics, such as accuracy and mean average precision (mAP).  
Conceptualization, H.C. and S.H.; methodology, H.C.; software, H.C.; validation, H.C.; formal analysis, S.H. and R.M.; data curation, H.C.; writing--original draft preparation, H.C. and S.H.; writing--review and editing, H.C., S.H. and R.M.; visualization, H.C.; supervision, X.X. project administration, X.X. and P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China No. 2020 YFB1806905, National Natural Science Foundation of China No. 62401074, Beijing Natural Science Foundation No. L242012, and the Major Key Project of Peng Cheng Laboratory (PCL) Department of Broadband Communication.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.