Latent-state-based spectrum resource allocation for UAV-assisted cognitive IoV

The rapid growth of cognitive Internet of Vehicles (IoV) networks requires efficient spectrum sharing mechanisms to support dynamic vehicular communications under limited spectrum resources. UAV-assisted cognitive relaying provides flexible coverage enhancement for vehicular networks. However, highly dynamic air-to-ground channels, vehicle mobility, and primary user (PU) protection constraints make long-term resource scheduling highly challenging. Conventional optimization methods and reactive reinforcement learning approaches often suffer from limited adaptability under dynamic network conditions due to their short-term decision mechanisms. To address these challenges, this paper proposes a predictive resource scheduling framework based on latent-state world modeling for UAV-assisted cognitive IoV networks. Specifically, the high-dimensional channel and vehicular states are mapped into a compact latent representation to capture temporal dependencies between channel evolution and queue dynamics. Based on the learned latent states, a VoI-aware predictive scheduling strategy is developed to evaluate the long-term effects of scheduling actions on information freshness and buffer stability through multi-step latent-state prediction. The proposed framework improves scheduling robustness under incomplete channel state information (CSI) and dynamic wireless environments. Simulation results under different signal-to-noise ratio (SNR) conditions and traffic loads demonstrate that the proposed framework achieves improved VoI-oriented scheduling performance and spectrum utilization compared with conventional model-free reinforcement learning baselines such as TD3 and PPO. These results confirm the effectiveness of integrating latent-state prediction into proactive spectrum resource scheduling for cognitive vehicular networks.