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Deep Learning Enabled Dynamic Reactive Video Caching in Mobile Edge Networks.

, , , , and . ICCS, page 280-285. IEEE, (2018)

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Computation Offloading with Virtual Resources Management in Mobile Edge Networks., , , , , and . VTC Spring, page 1-5. IEEE, (2018)Context-Aware Caching with Social Behavior in MEC-Enabled Wireless Cellular Networks., , and . PerCom Workshops, page 1004-1008. IEEE, (2019)Demo abstract: Context-aware video streaming with Q-learning for MEC-enabled cellular networks., , , and . INFOCOM Workshops, page 1-2. IEEE, (2018)Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding., , , , , , and . ICML, OpenReview.net, (2024)GenDT: mobile network drive testing made efficient with generative modeling., , , and . CoNEXT, page 43-58. ACM, (2022)Macro-Assisted Millimeter-Wave Small Cell Discovery in Ultra Dense Wireless Network., , , and . iThings/GreenCom/CPSCom/SmartData, page 545-551. IEEE Computer Society, (2017)Delay-Aware Resource Management for Heterogeneous Service Collaboration in Mobile Edge Networks., , , and . ICC Workshops, page 1-6. IEEE, (2018)Deep Learning Enabled Dynamic Reactive Video Caching in Mobile Edge Networks., , , , and . ICCS, page 280-285. IEEE, (2018)AppShot: A Conditional Deep Generative Model for Synthesizing Service-Level Mobile Traffic Snapshots at City Scale., , , , , and . IEEE Trans. Netw. Serv. Manag., 19 (4): 4136-4150 (December 2022)