Robust V2I Channel Prediction: A Generative Approach with Implicit State Evolution

Intelligent transportation plays a vital role in modern urban sustainability by enhancing traffic efficiency, ensuring safety, and mitigating environmental impact. Vehicle-to-Everything (V2X) technology, particularly Vehicle-to-Infrastructure (V2I), is fundamental to this vision, enabling seamless collaboration between vehicles and infrastructure. However, reliable communication in V2I faces challenges due to high-speed mobility, dynamic environments, and stringent latency requirements. While channel alignment methods such as scanning, tracking, and prediction offer some solutions, they struggle with efficiency and adaptability in real-world conditions. To address these limitations, this paper proposes a channel prediction model based on generative learning with implicit state evolution, capturing nonlinear mappings between channel state information and communication dynamics. Additionally, a side information module incorporating temporal and spatial data enhances adaptability to varying traffic and environmental conditions. Extensive evaluations across different weather and traffic densities demonstrate the model's robustness and superior predictive accuracy. The proposed approach provides a reliable and efficient solution for dynamic V2I communication, offering new insights for future intelligent transportation systems.