Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

翻译：未来的6G网络旨在通过增强移动增强现实（MAR）设备与边缘服务器之间的协作，来支持边缘辅助的移动增强现实。为了提供沉浸式的用户体验，MAR设备必须及时将相机帧上传至边缘服务器，以进行基于同步定位与建图（SLAM）的设备姿态追踪。本文针对用户特定且非平稳的上行数据流量，提出了一种基于数字孪生（DT）的用户中心化MAR通信服务供给方法。具体而言，为建立单个MAR设备的数字孪生，我们首先构建了一个为MAR定制的数据模型，该模型捕捉了基于SLAM的帧上传机制对用户特定数据流量模式的复杂影响。随后，我们定义了两个数字孪生操作函数，它们协同工作，能够自适应地在不同数据驱动模型之间切换，以捕捉非平稳的数据流量。利用数字孪生引入的面向用户的数据管理，我们提出了一种网络资源管理算法，该算法确保了帧上传的及时性，并增强了对单个MAR设备数据流量建模中固有不准确性的鲁棒性。基于真实轨迹的仿真结果表明，与5G网络中广泛使用的基于切片的通信服务供给相比，用户中心化的通信服务供给在满足相机帧上传延迟要求方面实现了14.2%的提升。