AI-Empowered Channel Generation for IoV Semantic Communications in Dynamic Conditions
Hao Liu, Bo Yang, Zhiwen Yu, Xuelin Cao, George C. Alexandropoulos, Yan Zhang, and Chau Yuen

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…
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