  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.
