Digital Twin-Based Joint Optimization Strategy for Dual Time Domain Slice Resource Management and DT Deployment in IoV

To address the diverse user service requirements in network slicing (NS) and the challenges of synchronization accuracy and low latency in Digital Twin (DT) deployment, this paper proposes a joint optimization strategy for digital twin-based dual-time domain slice resource management and DT deployment in the Internet of Vehicles (IoV). First, a demand-driven slice resource management scheme is introduced to mitigate Quality of Service (QoS) degradation caused by insufficient resource allocation under fluctuating user demands. Network performance indicator weights are dynamically adjusted using demand enhancement factors. Second, to minimize the impact of DT synchronization on resource allocation and address challenges like low latency and delay bias, DT utility is quantified from three perspectives: completeness, load contribution, and resource reliability. Finally, a price incentive mechanism with dynamic load adjustment is designed to balance the supply and demand of DT and service resources. This joint optimization problem is an NP-hard mixed-integer nonlinear problem with dual time-domain coupling, which can be decomposed into utility maximization strategies in different time domains. In the long-time domain, a Q-value-based Deep Transfer Reinforcement Learning (QDTRL) algorithm is used for DT deployment, while in the short-time domain, a Long Short-Term Memory - Multi-Agent Proximal Policy Optimization (LSTM-MAPPO) algorithm is applied for resource allocation and DT synchronization weight adjustment. Simulation results show that, compared to baseline schemes, the proposed strategy achieves higher utility, accelerates convergence, and effectively allocates resources and synchronizes DT.