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

TL;DR

This paper introduces an AI-driven semantic communication framework for IoV that uses generative models to predict dynamic wireless channel states, improving data transmission efficiency and adaptability in unpredictable environments.

Contribution

It proposes a novel semantic communication model utilizing generative diffusion for channel estimation and fine-tuning for scenario adaptability in IoV.

Findings

Enhanced channel prediction accuracy in dynamic conditions

Improved data transmission efficiency in IoV networks

Robustness of the model across different scenarios

Abstract

The Internet of Vehicles (IoV) transforms the transportation ecosystem promising pervasive connectivity and data-driven approaches. Deep learning and generative Artificial Intelligence (AI) have the potential to significantly enhance the operation of applications within IoV by facilitating efficient decision-making and predictive capabilities, including intelligent navigation, vehicle safety monitoring, accident prevention, 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 and unpredictable wireless channel conditions. To address these challenges, this paper proposes a semantic communication framework based on channel perception to improve the accuracy and efficiency of data transmission. The semantic communication model extracts and compresses the information to be transmitted. In addition, the wireless channel is estimated by using a generative diffusion model, which is employed to predict the dynamic channel states, thereby improving the quality of IoV service. In dynamic scenarios, however, the channel estimation performance may be degraded when substantially new scenarios take place, which will adversely affect user experience. To mitigate this limitation, we employ a large model to fine-tune the channel generation model to enhance its adaptability for varying scenarios. The performance and reliability of the proposed framework are evaluated on the two public datasets.