标题： 基于扩散的强化学习用于车联网元宇宙中动态无人机辅助车辆双生迁移 显示英文标题

标题： Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses

摘要： 空中-地面一体化网络可以缓解地面交通网络的通信压力，并在道路基础设施（RSU）覆盖稀疏的偏远地区和用户对车辆服务需求高的市中心地区，提供6G支持的车辆元宇宙服务卸载。 车辆双胞胎（VTs）是物理车辆的数字双胞胎，以实现更加沉浸和真实的车辆服务，这些服务可以在RSU上卸载和更新，以管理和为乘客和驾驶员提供车辆元宇宙服务。 车辆的高移动性和RSU信号的有限覆盖范围要求VT迁移，以确保车辆离开RSU信号覆盖时的服务连续性。 然而，不均衡的VT任务迁移可能会使某些RSU过载，这可能导致服务延迟增加，从而影响用户的沉浸式体验。 在本文中，我们提出了一种动态无人飞行器（UAV）辅助的VT迁移框架，在空中-地面一体化网络中，UAV作为空中边缘服务器，在VT任务卸载期间协助地面RSU。 在该框架中，我们提出了一种基于扩散的强化学习（RL）算法，该算法可以高效地在UAV辅助的车辆网络中做出沉浸式VT迁移决策。 为了平衡RSU的工作负载并提高VT迁移质量，我们设计了一种基于启发式搜索策略的UAV新型动态路径规划算法。 仿真结果表明，基于扩散的RL算法在UAV辅助下优于其他基线方案。 显示英文摘要

摘要： 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.