Augmented reality (AR)-enabled Metaverse is a promising technique to provide immersive service experience for mobile users. However, the limited network resources and unpredictable wireless propagation environments are key design bottlenecks of AR-enabled Metaverse systems. Therefore, this paper presents a resource management framework for simultaneously transmitting and reflecting RIS (STAR-RIS)-assisted AR-enabled Metaverse, where the STAR-RIS is configured to improve the communication efficiency between AR users and the Metaverse server located at the base station (BS). Moreover, we formulate a service latency minimization problem via jointly optimizing the computation resource allocation of the BS, coefficient matrix of the STAR-RIS, central processing unit (CPU) frequency and transmit power of the AR users. To tackle the non-convex problem, we utilize an approximate method to transform it to a tractable form, and decouple the multi-dimensional variables via the alternating optimization method. Particularly, the optimal coefficient matrix is obtained by a penalty function-based method with proved convergence, the CPU frequencies of AR users are derived as the closed-form solution, and the transmit power of AR users and computation resource allocation of the BS are obtained by the Lagrange duality method and convex optimization theory. Finally, simulation results demonstrates that the proposed method achieves remarkable latency reduction than several benchmark methods.

翻译：增强现实（AR）赋能的元宇宙是一种为移动用户提供沉浸式服务体验的前沿技术。然而，有限的网络资源与不可预测的无线传播环境是AR赋能元宇宙系统的关键设计瓶颈。为此，本文提出了一种同时传输与反射智能超表面（STAR-RIS）辅助的AR赋能元宇宙资源管理框架，其中STAR-RIS被配置用于提升AR用户与位于基站（BS）的元宇宙服务器之间的通信效率。此外，我们通过联合优化BS的计算资源分配、STAR-RIS的系数矩阵、AR用户的中央处理器（CPU）频率与发射功率，构建了一个服务时延最小化问题。为处理这一非凸问题，我们采用近似方法将其转化为可处理形式，并通过交替优化方法解耦多维变量。具体而言，最优系数矩阵通过一种经证明收敛的基于罚函数的方法获得，AR用户的CPU频率以闭式解形式推导得出，而AR用户的发射功率与BS的计算资源分配则通过拉格朗日对偶方法与凸优化理论求解。最终，仿真结果表明，与多种基准方法相比，所提方案实现了显著的时延降低。