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NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things
Qian, Liping1,2; Wu, Yuan3,4; Jiang, Fuli3; Yu, Ningning5; Lu, Weidang5; Lin, Bin6
2021-08
Source PublicationIEEE Transactions on Industrial Informatics
ISSN1551-3203
Volume17Issue:8Pages:5688-5698
Abstract

Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.

KeywordDeep Reinforcement Learning Energy Consumption Optimization Multi-access Mobile Edge Computing Non-orthogonal Multiple Access
DOI10.1109/TII.2020.3001355
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS IDWOS:000647406400054
Scopus ID2-s2.0-85105560276
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Cited Times [WOS]:43   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorWu, Yuan
Affiliation1.College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China
2.National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China
3.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao
4.Department of Computer Information Science, University of Macau, Macao
5.College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China
6.College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, 116026, China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Qian, Liping,Wu, Yuan,Jiang, Fuli,et al. NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things[J]. IEEE Transactions on Industrial Informatics,2021,17(8):5688-5698.
APA Qian, Liping,Wu, Yuan,Jiang, Fuli,Yu, Ningning,Lu, Weidang,&Lin, Bin.(2021).NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things.IEEE Transactions on Industrial Informatics,17(8),5688-5698.
MLA Qian, Liping,et al."NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things".IEEE Transactions on Industrial Informatics 17.8(2021):5688-5698.
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