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

2025-07-02

AI Summary

To address low data transmission efficiency in vehicular networks (IoV) and the sharp degradation of channel estimation performance in unseen dynamic wireless environments, this paper proposes a channel-aware semantic communication framework. Methodologically, it integrates a generative diffusion model with online fine-tuning of large language models (LLMs) to achieve high-accuracy prediction of time-varying channel states; further, it combines semantic information extraction and compression to construct an end-to-end jointly optimized architecture. Its key contribution lies in being the first to incorporate generative AI–driven channel prediction and lightweight, adaptive LLM fine-tuning into semantic communication, substantially enhancing generalization to unknown scenarios. Experiments on two public datasets demonstrate that the framework reduces channel estimation error by 32.7% and improves semantic transmission efficiency by 2.1×, thereby ensuring service continuity and robustness for critical applications such as intelligent navigation and safety monitoring.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Enhance real-time data transmission in dynamic IoV environments
• Improve wireless channel estimation using generative AI models
• Adapt channel generation to varying scenarios for better reliability

Innovation

• Semantic communication framework for IoV data
• Generative diffusion model for channel prediction
• Large model fine-tuning for scenario adaptability