Resource Awareness in Unmanned Aerial Vehicle-Assisted Mobile-Edge Computing Systems

Xianfu Chen, Tao Chen, Zhifeng Zhao, Honggang Zhang, Mehdi Bennis, Yusheng Ji

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference (VTC Spring 2020)
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages6
ISBN (Electronic)978-1-7281-5207-3
ISBN (Print)978-1-7281-4053-7
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: 25 May 202028 May 2020

Publication series

SeriesIEEE Vehicular Technology Conference Proceedings


Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020



  • deep reinforcement learning
  • digital twin.
  • long shortterm memory
  • Mobile-edge computing
  • resource awareness
  • unmanned aerial vehicle


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