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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.
|Title of host publication||2020 IEEE 91st Vehicular Technology Conference (VTC Spring 2020)|
|Publisher||IEEE Institute of Electrical and Electronic Engineers|
|Number of pages||6|
|Publication status||Published - 2020|
|MoE publication type||A4 Article in a conference publication|
|Event||91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium|
Duration: 25 May 2020 → 28 May 2020
|Series||IEEE Vehicular Technology Conference|
|Conference||91st IEEE Vehicular Technology Conference, VTC Spring 2020|
|Period||25/05/20 → 28/05/20|
- deep reinforcement learning
- digital twin.
- long shortterm memory
- Mobile-edge computing
- resource awareness
- unmanned aerial vehicle
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- 2 Finished
1/01/19 → 31/12/21
Project: Academy of Finland project