A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection

Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This article considers a UAV-assisted Metaverse network, in which UAVs extend the coverag...

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Bibliographic Details
Published in:IEEE network 2023-07, Vol.37 (4), p.248-254
Main Authors: Si, Peiyuan, Qian, Liangxin, Zhao, Jun, Lam, Kwok-Yan
Format: Article
Language:English
Subjects:
Autonomous aerial vehicles
Autonomous vehicles
Completion time
Connected vehicles
Convex functions
Convexity
Data collection
Internet of Things
Metaverse
Optimization
Radio equipment
Reinforcement learning
Resource allocation
Resource management
Roadsides
Simulation
Time dependence
Trajectory control
Unmanned aerial vehicles
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Summary:Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This article considers a UAV-assisted Metaverse network, in which UAVs extend the coverage of the base station (BS) to collect the Metaverse data generated at roadside units (RSUs). Specifically, to improve the data collection efficiency, resource allocation and trajectory control are integrated into the system model. The time-dependent nature of the optimization problem makes it non-trivial to be solved by traditional convex optimization methods. Based on the proposed UAV-assisted Metaverse network system model, we design a hybrid framework with reinforcement learning and convex optimization to cooperatively solve the time-sequential optimization problem. Simulation results show that the proposed framework is able to reduce the mission completion time with a given transmission power resource.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.011.2300032