Energy-Delay Tradeoff for Online Offloading Based on Deep Reinforcement Learning in Wireless Powered Mobile-Edge Computing Networks

TP391.9; Benefited from wireless power transfer ( WPT ) and mobile-edge computing ( MEC ) , wireless powered MEC systems have attracted widespread attention. Specifically, we design an online offloading scheme based on deep reinforcement learning that maximizes the computation rate and minimizes the...

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Bibliographic Details
Published in东华大学学报（英文版） Vol. 37; no. 6; pp. 498 - 503
Main Authors WANG Zhonglin, CAO Hankai, ZHAO Ping, RAO Wei
Format Journal Article
LanguageEnglish
Published College of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315000, China%College of Information Science and Technology, Donghua University, Shanghai 201620, China%Tencent Media Lab, Shenzhen 518000, China 31.12.2020
Subjects
wireless power transfer (WPF)
deep reinforcement learning
computation offloading
mobile-edge computing (MEC)
energy consumption
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Summary:TP391.9; Benefited from wireless power transfer ( WPT ) and mobile-edge computing ( MEC ) , wireless powered MEC systems have attracted widespread attention. Specifically, we design an online offloading scheme based on deep reinforcement learning that maximizes the computation rate and minimizes the energy consumption of all wireless devices ( WDs) . Extensive results validate that the proposed scheme can achieve better tradeoff between energy consumption and computation delay.
ISSN:1672-5220
DOI:10.19884/j.1672-5220.202009072