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Deep Reinforcement Learning-Based Dynamic Offloading Management in UAV-Assisted MEC System

Tian, Kang ; Liu, Yameng ; Chai, Haojun ; Liu, Boyang Ning, Xin ; Xin Ning

Wireless communications and mobile computing, 2022-02, Vol.2022, p.1-13 [Periódico revisado por pares]

Oxford: Hindawi

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Título:
Deep Reinforcement Learning-Based Dynamic Offloading Management in UAV-Assisted MEC System
Autor: Tian, Kang ; Liu, Yameng ; Chai, Haojun ; Liu, Boyang
Ning, Xin ; Xin Ning
Assuntos: Algorithms ; Communication ; Computation offloading ; Deep learning ; Design ; Edge computing ; Energy consumption ; Ground stations ; Machine learning ; Mobile computing ; Optimization ; Performance evaluation ; Power ; Relaying ; Scheduling ; Servers ; Simulation ; Unmanned aerial vehicles ; Video compression ; Wireless networks
É parte de: Wireless communications and mobile computing, 2022-02, Vol.2022, p.1-13
Descrição: Unmanned aerial vehicles (UAVs) have been envisioned as a promising technique to provide relaying and mobile edge computing (MEC) services for ground user equipment (UE). In this paper, we propose a UAV-assisted MEC architecture in dynamic environment, where a UAV flies with a fixed trajectory and may act as a MEC server to process the tasks offloaded from the UE or act as a relay to help the UE to offload their tasks to the ground base station (BS). The objective of this work is to maximize the long-term number of completed tasks of the UE. An optimization problem is formulated to optimize the task offloading decisions of the UE. Considering the random demands of the UE, a deep reinforcement learning- (DRL-) based algorithm is proposed to solve the formulated nonconvex optimization problem. Simulation results verify the effectiveness and correctness of the proposed algorithm.
Editor: Oxford: Hindawi
Idioma: Inglês

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Deep Reinforcement Learning-Based Dynamic Offloading Management in UAV-Assisted MEC System

Tian, Kang ; Liu, Yameng ; Chai, Haojun ; Liu, Boyang Ning, Xin ; Xin Ning

Oxford: Hindawi

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