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Delay-Sensitive Task Offloading for Internet of Things in Nonorthogonal Multiple Access MEC Networks

Authors
Tuong Van DatTruong Thanh PhungTran, Anh-TienMasood, Arooj.Lakew, Demeke ShumeyeLee, Chunghyun.Lee, Yunseong.Cho, Sungrae
Issue Date
Oct-2020
Publisher
IEEE Computer Society
Keywords
Delay-sensitive task offloading; mobile edge computing; nonorthogonal multiple access; reinforcement learning; resource allocation
Citation
International Conference on ICT Convergence, v.2020, no.October, pp 597 - 599
Pages
3
Journal Title
International Conference on ICT Convergence
Volume
2020
Number
October
Start Page
597
End Page
599
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44062
DOI
10.1109/ICTC49870.2020.9289406
ISSN
2162-1233
Abstract
With the rapid development of the Internet of Things (IoTs), the fifth-generation (5G) networks need to serve massive connection and accommodate ultra-low delay. In response to these challenges, mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) have been considered as the promising solutions. In this paper, we investigate the joint optimization problem of computation offloading and resource allocation in NOMA MEC networks to minimize the delay to complete tasks of all users. Different from the conventional optimization approach, we propose and develop an online solution based on deep reinforcement learning (DRL) algorithm, which can fit with dynamic networks with time-varying channels. In particular, we employ deep neural networks (DNNs) to process the raw state inputs and then output the computation offloading decision and resource allocation at different times. The weights of DNNs are continuously trained with the observed data via interactions with the environment. Simulation results reveal that our proposed algorithm achieves higher delay reduction compared to the existing strategies. © 2020 IEEE.
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Cho, Sung Rae
소프트웨어대학 (소프트웨어학부)
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