Improving the interactivity and interconnectivity between people is one of the highlights of the Metaverse. The Metaverse relies on a core approach, digital twinning, which is a means to replicate physical world objects, people, actions and scenes onto the virtual world. Being able to access scenes and information associated with the physical world, in the Metaverse in real-time and under mobility, is essential in developing a highly accessible, interactive and interconnective experience for all users. This development allows users from other locations to access high-quality real-world and up-to-date information about events happening in another location, and socialize with others hyper-interactively. Nevertheless, receiving continual, smooth updates generated by others from the Metaverse is a challenging task due to the large data size of the virtual world graphics and the need for low latency transmission. With the development of Mobile Augmented Reality (MAR), users can interact via the Metaverse in a highly interactive manner, even under mobility. Hence in our work, we considered an environment with users in moving Internet of Vehicles (IoV), downloading real-time virtual world updates from Metaverse Service Provider Cell Stations (MSPCSs) via wireless communications. We design an environment with multiple cell stations, where there will be a handover of users' virtual world graphic download tasks between cell stations. As transmission latency is the primary concern in receiving virtual world updates under mobility, our work aims to allocate system resources to minimize the total time taken for users in vehicles to download their virtual world scenes from the cell stations. We utilize deep reinforcement learning and evaluate the performance of the algorithms under different environmental configurations. Our work provides a use case of the Metaverse over AI-enabled 6G communications.

中文翻译：

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基于 6G 无线通信的车联网移动元界资源分配：一种深度强化学习方法

提高人与人之间的交互性和互联性是元界的亮点之一。Metaverse 依赖于一种核心方法，即数字孪生，这是一种将物理世界对象、人物、动作和场景复制到虚拟世界的方法。能够在元界中实时和移动地访问与物理世界相关的场景和信息，对于为所有用户开发高度可访问、交互和互连的体验至关重要。这种开发允许来自其他位置的用户访问有关在另一个位置发生的事件的高质量真实世界和最新信息，并与其他人进行超交互式社交。然而，不断收到，由于虚拟世界图形的数据量很大并且需要低延迟传输，因此元界其他人生成的平滑更新是一项具有挑战性的任务。随着移动增强现实（MAR）的发展，即使在移动性下，用户也可以通过元界以高度交互的方式进行交互。因此，在我们的工作中，我们考虑了用户移动车联网 (IoV) 的环境，通过无线通信从元界服务提供商基站 (MSPCS) 下载实时虚拟世界更新。我们设计了一个有多个基站的环境，用户的虚拟世界图形下载任务将在基站之间进行切换。由于传输延迟是在移动性下接收虚拟世界更新的主要关注点，我们的工作旨在分配系统资源，以最大限度地减少车辆用户从基站下载他们的虚拟世界场景所花费的总时间。我们利用深度强化学习并评估算法在不同环境配置下的性能。我们的工作提供了一个基于 AI 的 6G 通信的 Metaverse 用例。