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Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services.
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2014
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R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction” . Cambridge, MA, USA: MIT Press, 2016
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Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, “A Survey on Mobile Edge Computing: The Communication Perspective”, in IEEE Commun. Surv. Tutorials , vol. 19, no.4, pp. 2322-2358, Aug. 2017
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2017
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M. Chen and Y. Hao, “Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network,” in IEEE J. Sel. Areas Commun. , vol. 36, no. 3, pp. 587-597, Mar. 2018
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A. Sadeghi, F. Sheikholeslami, and G. B. Giannakis, “Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities,” in IEEE J. Sel. Top. Signal Process. , vol. 12, no. 1, pp. 180-190, Feb. 2018
2018
Closest in time.
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2018
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X. Li, X. Wang, P.-J. Wan, Z. Han, V. C.M. Leung, “Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design”, IEEE Journal on Selected Areas in Communications , Special Issue on Caching for Communication Systems and Networks, Early Access, 2018
2018
Closest in time.
E. Li, Z. Zhou, X. Chen, “Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy,” in ACM SIGCOMM, MECOMM Workshop , 2018
2018
Closest in time.
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F. Tang, B. Mao, Z. M. Fadlullah, N. Kato, O. Akashi, T. Inoue, and K. Mizutani, “On Removing Routing Protocol from Future Wireless Networks: A Real-Time Deep Learning Approach for Intelligent Traffic Control,” in IEEE Wireless Communications , vol. 25, no. 1, pp. 154-160, Feb. 2018
2018
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H. Van Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q-Learning,” in Proceedings of the 30th AAAI Conference on Artificial Intelligence , vol. 16, 2016, pp. 2094-2100
2094
Closest in time.