# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 1 Introduction
### Motivations and Contributions  
While these research results provide significant theoretical and technical support for enhancing the efficiency and reliability of satellite-ground communication systems, several issues remain in the image transmission algorithm within the satellite-ground scenario using SemCom. Firstly, unlike traditional ground networks, LEO satellites are in constant high-speed motion, resulting in a continuously changing communication environment with the ground. The fast fluctuating channel is characterized by significant changes in channel parameters over short periods, making it challenging for traditional satellite-ground image transmission models to adapt in real time, resulting in a decline in transmission performance. Additionally, fast fluctuation channels are typically accompanied by multi-path fading, which causes signal interference and jitter, negatively impacting the stability and quality of image transmission. Furthermore, the spectrum compression effect is less pronounced in fast fluctuation channels compared to stable channels, thereby limiting the effectiveness of satellite-ground image transmission models in high-data-rate scenarios. This dynamic environment necessitates that the image transmission algorithm using SemCom consider more samples of different states during model training. Particularly for transmission algorithms employing pre-trained models, discrepancies between actual channel conditions and training conditions can lead to a rapid decline in performance. Although current iterative decoding methods [28; 29; 30] can accurately correct errors and improve the quality of transmitted information by combining semantic and syntactic information, they require multiple calculations. This significantly increases computational complexity and time overhead, rendering them unsuitable for deployment in satellite-ground scenarios where computing power and delay are limited. Secondly, current SemCom algorithms based on deep learning are typically designed and trained with a joint encoder-decoder configuration. Consequently, any changes in the decoder configuration require corresponding adjustments in the encoder configuration. However, in satellite-ground transmission, a satellite often needs to serve multiple users simultaneously. Constantly modifying the encoder configuration on the satellite side for users in different regions is impractical. Thus, it is imperative to develop a channel-adaptive transmission model that does not necessitate modifications to the satellite-side configuration. Against this background, we propose an adaptive transmission architecture based on Channel Code-Book (CCB). The proposed framework records different channel information in the code-book in the form of parameters, obtains the current actual environmental information through the pilot signal, matches and denoises the information stored in the code-book, and finally realizes the data restoration. The main contributions of this paper are listed as follows:  
* Addressing the issue of poor adaptability in image transmission models due to rapid channel changes in satellite-ground environments, we propose a SemCom framework equipped with the CCB. The framework enables reliable image transmission in various complex environments;
* To tackle the problem of signal distortion resulting from channel fading and noise, we design an Adaptive Channel Denoising Model (ACDM) as a denoising solution. Thismodel can adaptively choose the appropriate denoising parameters based on different signal characteristics, effectively restoring faded and noisy images in satellite-ground channel environments;
* To enable the denoising model to select suitable parameters based on the current channel environment, we propose a pilot-based method for channel model parameter selection.