University of Oulu

X. Chen, T. Chen, Z. Zhao, H. Zhang, M. Bennis and Y. JI, "Resource Awareness In Unmanned Aerial Vehicle-Assisted Mobile-Edge Computing Systems," 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-6, doi: 10.1109/VTC2020-Spring48590.2020.9128981

Resource awareness in unmanned aerial vehicle-assisted mobile-edge computing systems

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Author: Chen, Xianfu1; Chen, Tao1; Zhao, Zhifeng2;
Organizations: 1VTT Technical Research Centre of Finland Ltd, Finland
2Research Center for Intelligent Networks, Zhejiang Lab, Hangzhou, China
3College of Information Science and Electronic Engineering, Zhejiang University, China
4Centre for Wireless Communications, University of Oulu, Finland
5Information Systems Architecture Research Division, National Institute of Informatics, Tokyo, Japan
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-18


This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.

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Series: IEEE Vehicular Technology Conference
ISSN: 1090-3038
ISSN-L: 1090-3038
ISBN: 978-1-7281-5207-3
ISBN Print: 978-1-7281-4053-7
Article number: 9128981
DOI: 10.1109/VTC2020-Spring48590.2020.9128981
Host publication: 91st IEEE Vehicular Technology Conference, VTC Spring 2020
Conference: IEEE Vehicular Technology Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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