SA-DJSCC: Scenario Adaptive Deep Joint Source-Channel Coding for Wireless Image Transmission

Semantic communication is emerging as a promising paradigm for the future of wireless communication, with recent progress in deep learning-based joint source-channel coding (JSCC) achieving notable success. However, the performance of wireless communication systems is often constrained by the variability and dynamics of channel conditions, which presents significant challenges for system robustness and adaptability. In real-world applications, such as in railway environments where multiple diverse scenarios are encountered, including rural, viaduct, tunnel and hilly terrain scenarios, the variability of the channel conditions can be particularly pronounced. Existing deep learning-based JSCC approaches typically train models in fixed channel models, limiting their ability to generalize across different channel scenarios. As a result, a model trained for one specific channel type is ineffective in others, necessitating the deployment of distinct models for different channel models. To address this issue, we propose a novel channel scenario adaptive deep joint source-channel coding (SA-DJSCC) method that integrates the channel type label into both the training and inference phases. By incorporating channel scenario information into the JSCC model, our approach allows a single model to effectively generalize across multiple channel models. Experimental results show that our method improves scenario adaptability and outperforms existing approaches across different channel models and signal-to-noise ratio conditions. This work represents a key advancement towards developing more resilient and efficient semantic communication systems capable of operating in dynamic wireless environments.