UM
Latency Optimization for Computation Offloading with Hybrid NOMA-OMA Transmission
Liu,Lina1; Sun,Bo2; Wu,Yuan3; Tsang,Danny H.K.2
2021
Source PublicationIEEE Internet of Things Journal
AbstractThe Internet of Things (IoT) platform is faced with critical challenges posed by the conflict between resource-hungry IoT applications and resource-constrained IoT devices. Mobile edge computing (MEC) provides a promising solution by allowing IoT devices to offload their computation to nearby edge servers to enable fast and energy-efficient data processing. In this paper, we study a scenario where two IoT users (IoT devices) offload their computation workloads to an edge server with hybrid non-orthogonal multiple access (NOMA)-orthogonal multiple access (OMA) transmission. The hybrid multiple access transmission incorporates three offloading methods, namely, hybrid NOMA, pure NOMA, and pure OMA. The offloading-method selection, together with user selection which determines the roles played by different IoT users in data transmission, comprises our offloading strategy, and is optimized to minimize the maximal offloading latency of the two IoT users. By exploiting the method of successive convex approximation (SCA), we design an efficient algorithm to solve the complicated non-convex problem and rigorously prove the convergence of our algorithm. Extensive numerical tests show that our scheme can always help IoT users to flexibly choose the best offloading strategy. Inspired by experimental observations, we analytically establish the criteria for the three offloading methods. We show that pure OMA transmission is never the best offloading method, except in some extreme cases that rarely occur in practice, while pure NOMA transmission is the most desirable offloading method in terms of latency minimization. We then propose detection approaches for the best offloading strategy with both offloading-method selection and user selection under certain system settings. The user selection is applied to avoid the pure OMA transmission and encourage the pure NOMA transmission.
KeywordApproximation algorithms Computation offloading hybrid NOMA-OMA transmission Internet of Things Internet of Things (IoT). latency minimization Minimization NOMA Resource management Servers Silicon carbide
DOI10.1109/JIOT.2021.3055510
URLView the original
Language英语
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China. (e-mail: lliuaw@connect.ust.hk)
2.Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
3.State Key Laboratory of Internet of Things for Smart City, The University of Macau, and also with Department of Computer and Information Science, University of Macau, Macau SAR, China.
Recommended Citation
GB/T 7714
Liu,Lina,Sun,Bo,Wu,Yuan,et al. Latency Optimization for Computation Offloading with Hybrid NOMA-OMA Transmission[J]. IEEE Internet of Things Journal,2021.
APA Liu,Lina,Sun,Bo,Wu,Yuan,&Tsang,Danny H.K..(2021).Latency Optimization for Computation Offloading with Hybrid NOMA-OMA Transmission.IEEE Internet of Things Journal.
MLA Liu,Lina,et al."Latency Optimization for Computation Offloading with Hybrid NOMA-OMA Transmission".IEEE Internet of Things Journal (2021).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu,Lina]'s Articles
[Sun,Bo]'s Articles
[Wu,Yuan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu,Lina]'s Articles
[Sun,Bo]'s Articles
[Wu,Yuan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu,Lina]'s Articles
[Sun,Bo]'s Articles
[Wu,Yuan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.