Wireless Powered Metaverse: Joint Task Scheduling and Trajectory Design for Multi-Devices and Multi-UAVs

TL;DR

This paper introduces a multi-task deep reinforcement learning approach for joint task scheduling and trajectory optimization in UAV-assisted wireless powered metaverse applications, enhancing computation efficiency.

Contribution

It proposes a novel two-stage optimization algorithm combining heuristic methods and multi-task DRL for joint resource scheduling in complex UAV-enabled wireless powered metaverse systems.

Findings

Significant improvements in convergence speed.

Higher average computation efficiency.

Effective joint scheduling and trajectory design.

Abstract

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.