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
Online-Zugang:	Volltext
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