The rise of 6G-enable Vehicular Metaverses is transforming the automotive industry by integrating immersive, real-time vehicular services through ultra-low latency and high bandwidth connectivity. In 6G-enable Vehicular Metaverses, vehicles are represented by Vehicle Twins (VTs), which serve as digital replicas of physical vehicles to support real-time vehicular applications such as large Artificial Intelligence (AI) model-based Augmented Reality (AR) navigation, called VT tasks. VT tasks are resource-intensive and need to be offloaded to ground Base Stations (BSs) for fast processing. However, high demand for VT tasks and limited resources of ground BSs, pose significant resource allocation challenges, particularly in densely populated urban areas like intersections. As a promising solution, Unmanned Aerial Vehicles (UAVs) act as aerial edge servers to dynamically assist ground BSs in handling VT tasks, relieving resource pressure on ground BSs. However, due to high mobility of UAVs, there exists information asymmetry regarding VT task demands between UAVs and ground BSs, resulting in inefficient resource allocation of UAVs. To address these challenges, we propose a learning-based Modified Second-Bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs by accounting for VT task latency and accuracy. Moreover, we design a diffusion-based reinforcement learning algorithm to optimize the price scaling factor, maximizing the total surplus of resource providers and minimizing VT task latency. Finally, simulation results demonstrate that the proposed diffusion-based MSB auction outperforms traditional baselines, providing better resource distribution and enhanced service quality for vehicular users.

翻译：6G赋能车载元宇宙的兴起正在通过超低延迟和高带宽连接整合沉浸式实时车载服务，从而变革汽车行业。在6G赋能车载元宇宙中，车辆由车辆数字孪生体（Vehicle Twins, VTs）作为物理车辆的数字化副本进行表征，以支持实时车载应用，例如基于大型人工智能（AI）模型的增强现实（AR）导航（称为VT任务）。VT任务属于资源密集型任务，需要卸载到地面基站（Base Stations, BSs）进行快速处理。然而，VT任务的高需求与地面基站有限资源之间的矛盾，尤其是在交叉口等高密度城市区域，带来了显著的资源分配挑战。作为一种有前景的解决方案，无人机（Unmanned Aerial Vehicles, UAVs）作为空中边缘服务器动态协助地面基站处理VT任务，缓解了地面基站的资源压力。然而，由于无人机的高移动性，无人机与地面基站之间关于VT任务需求存在信息不对称，导致无人机的资源分配效率低下。为应对这些挑战，我们提出了一种基于学习的改进第二价格（Modified Second-Bid, MSB）拍卖机制，通过考虑VT任务延迟和准确性来优化地面基站与无人机之间的资源分配。此外，我们设计了一种基于扩散的强化学习算法来优化价格缩放因子，以最大化资源提供者的总剩余并最小化VT任务延迟。最后，仿真结果表明，所提出的基于扩散的MSB拍卖机制优于传统基线方法，能为车载用户提供更优的资源分配和更高的服务质量。