To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency.

翻译：为支持在移动设备上运行以人为中心的元宇宙应用，无人机辅助的无线赋能移动边缘计算有望弥补移动设备在计算能力和能源供应方面的局限性。元宇宙应用对高速计算处理的需求和巨大的能量消耗要求对多设备与多无人机进行联合资源调度，但现有的无线赋能移动边缘计算方案由于组合优化的复杂性，仅能处理设备或无人机的单独调度。为应对上述挑战，我们提出一种基于多任务深度强化学习的两阶段交替优化算法，在无线赋能元宇宙场景中联合分配充电时间、调度计算任务，并优化无人机与移动设备的轨迹。首先，考虑无人机与移动设备的能量约束，我们构建了一个优化问题以最大化系统计算效率。其次，提出一种启发式算法来高效执行移动设备的时间分配与充电调度。随后，设计了一种多任务深度强化学习方案，用于制定无人机的充电调度与轨迹设计决策。最后，理论分析与性能结果表明，相较于代表性方法，本算法在收敛速度与平均计算效率方面具有显著优势。