Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are si...

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Vydané v:IEEE journal on selected areas in communications Ročník 36; číslo 9; s. 1927 - 1941
Hlavní autori: Zhou, Fuhui, Wu, Yongpeng, Hu, Rose Qingyang, Qian, Yi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
binary computation offloading
Computation offloading
Computer architecture
Computer simulation
Edge computing
Energy harvesting
Maximization
Mobile computing
Mobile handsets
Mobile-edge computing
Optimization
partial computation offloading
Resource allocation
Resource management
Task analysis
unmanned aerial vehicle-enabled
Unmanned aerial vehicles
Wireless communication
Wireless communications
wireless power transfer
Wireless power transmission
ISSN:0733-8716, 1558-0008
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Popis
Shrnutí:Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2018.2864426