Online Computation Offloading in NOMA-Based Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

One of the missions of fifth generation (5G) wireless networks is to provide massive connectivity of the fast growing number of Internet of Things (IoT) devices. To satisfy this mission, non-orthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantl...

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Veröffentlicht in:IEEE access Jg. 8; S. 99098 - 99109
Hauptverfasser: Nduwayezu, Maurice, Pham, Quoc-Viet, Hwang, Won-Joo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
5G mobile communication
5G networks
Algorithms
Computation offloading
Deep learning
deep reinforcement learning (DRL)
Edge computing
Internet of Things
Machine learning
Mobile computing
multi access edge computing (MEC)
New technology
NOMA
non-orthogonal multiple access (NOMA)
Nonorthogonal multiple access
online computation offloading
Optimization
Resource management
Subcarriers
Task analysis
Wireless communication
Wireless networks
ISSN:2169-3536, 2169-3536
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Zusammenfassung:One of the missions of fifth generation (5G) wireless networks is to provide massive connectivity of the fast growing number of Internet of Things (IoT) devices. To satisfy this mission, non-orthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. Considered as a booster of IoT devices, and in parallel with the development of NOMA techniques, multi-access edge computing (MEC) is also becoming one of the key emerging technologies for 5G networks. In this paper, with an objective of maximizing the computation rate of an MEC system, we investigate the computation offloading and subcarrier allocation problem in Multi-carrier (MC) NOMA based MEC systems and address it using Deep Reinforcement Learning for Online Computation Offloading (DRLOCO-MNM) algorithm. In particular, the DRLOCO-MNM helps each of the user equipments (UEs) decides between local and remote computation modes, and also assigns the appropriate subcarrier to the UEs in the case of remote computation mode. The DRLOCO-MNM algorithm is especially advantageous over the other machine learning techniques applied on NOMA because it does not require labeled data for training or a complete definition of the channel environment. The DRLOCO-MNM also does avoid the complexity found in many optimization algorithms used to solve channel allocation in existing NOMA related studies. Numerical simulations and comparison with other algorithms show that our proposed module and its algorithm considerably improve the computation rates of MEC systems.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2997925