Air-ground integrated networks can relieve communication pressure on ground
transportation networks and provide 6G-enabled vehicular Metaverses services
offloading in remote areas with sparse RoadSide Units (RSUs) coverage and
downtown areas where users have a high demand for vehicular services. Vehicle
Twins (VTs) are the digital twins of physical vehicles to enable more immersive
and realistic vehicular services, which can be offloaded and updated on RSU, to
manage and provide vehicular Metaverses services to passengers and drivers. The
high mobility of vehicles and the limited coverage of RSU signals necessitate
VT migration to ensure service continuity when vehicles leave the signal
coverage of RSUs. However, uneven VT task migration might overload some RSUs,
which might result in increased service latency, and thus impactive immersive
experiences for users. In this paper, we propose a dynamic Unmanned Aerial
Vehicle (UAV)-assisted VT migration framework in air-ground integrated
networks, where UAVs act as aerial edge servers to assist ground RSUs during VT
task offloading. In this framework, we propose a diffusion-based Reinforcement
Learning (RL) algorithm, which can efficiently make immersive VT migration
decisions in UAV-assisted vehicular networks. To balance the workload of RSUs
and improve VT migration quality, we design a novel dynamic path planning
algorithm based on a heuristic search strategy for UAVs. Simulation results
show that the diffusion-based RL algorithm with UAV-assisted performs better
than other baseline schemes.

本文提出了一种基于动态无人机辅助的车辆双生物迁移框架，使用空地一体化网络中的无人机作为辅助服务器，以平衡 RSU 的负载并提高双生物迁移质量。模拟结果表明，与其他基准方案相比，基于扩散式强化学习算法和无人机辅助的方法效果更好。