Lyapunov-Guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 20; H. 11; S. 7519 - 7537
Hauptverfasser:	Bi, Suzhi, Huang, Liang, Wang, Hui, Zhang, Ying-Jun Angela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Arrivals Computation offloading Computational modeling Data processing Decisions Deep learning deep reinforcement learning Edge computing Frames (data processing) Linear programming Lyapunov optimization Machine learning Mixed integer Mobile computing Mobile edge computing Nonlinear programming Optimization Queues Resource allocation Resource management Stability analysis Stochastic processes Task analysis Wireless communication
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
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