AI-Empowered Channel Generation for IoV Semantic Communications in Dynamic Conditions

The Internet of Vehicles (IoV) is transforming the transportation ecosystem, enabling pervasive connectivity and data-driven approaches. Deep learning and generative artificial intelligence (GAI) have the potential to significantly enhance IoV application performance by enabling efficient decision- making and predictive capabilities, including intelligent navigation, vehicle safety monitoring, and intelligent traffic management. Nevertheless, efficiently transmitting and processing the massive volumes of data generated by the IoV in real time remains a significant challenge, particularly in dynamic, unpredictable wireless channel conditions. To address these challenges, this paper proposes a semantic communication (SC) framework based on a CNN-Transformer architecture that provides Channel estimation via Diffusion (CTCD) to improve the accuracy and efficiency of data transmission. The SC model extracts and compresses the information to be transmitted. The wireless channel is then estimated using a generative diffusion model to predict dynamic channel states, thereby improving the quality of IoV services. In dynamic scenarios, however, channel estimation performance may degrade when substantially new scenarios occur, adversely affecting the user experience. To mitigate this limitation, we fine-tune the channel generation model to enhance its adaptability across varying scenarios. The performance and reliability of the proposed framework are evaluated on two public image datasets, and the framework outperforms the comparative methods in image reconstruction metrics.