Metaverse is expected to create a virtual world closely connected with reality to provide users with immersive experience with the support of 5G high data rate communication technique. A huge amount of data in physical world needs to be synchronized to the virtual world to provide immersive experience for users, and there will be higher requirements on coverage to include more users into Metaverse. However, 5G signal suffers severe attenuation, which makes it more expensive to maintain the same coverage. Unmanned aerial vehicle (UAV) is a promising candidate technique for future implementation of Metaverse as a low-cost and high-mobility platform for communication devices. In this paper, we propose a proximal policy optimization (PPO) based double-agent cooperative reinforcement learning method for channel allocation and trajectory control of UAV to collect and synchronize data from the physical world to the virtual world, and expand the coverage of Metaverse services economically. Simulation results show that our proposed method is able to achieve better performance compared to the benchmark approaches.
翻译:预计元数据将创造一个与现实密切相关的虚拟世界,为用户提供5G高数据速率通信技术支持的亲身体验。物理世界的大量数据需要与虚拟世界同步,以便为用户提供亲身体验,对覆盖的要求将更高,将更多用户纳入元数据元。然而,5G信号受到严重减缩,因此维持同样的覆盖成本更高。无人驾驶飞行器(UAV)是未来实施Metevevy作为低成本高流动性通信设备平台的有希望的候选技术。在本文件中,我们提议采用基于双剂政策优化的双剂合作强化学习方法,用于将UAV的频道分配和轨迹控制纳入虚拟世界,以收集和同步从物理世界到虚拟世界的数据,并扩大Metverse服务的经济覆盖面。模拟结果表明,与基准方法相比,我们提出的方法能够取得更好的业绩。