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dc.contributor.authorChiu, Fengen_US
dc.contributor.authorKuo, Ting-Yuen_US
dc.contributor.authorChien, Feng-Tsunen_US
dc.contributor.authorHuang, Wan-Jenen_US
dc.contributor.authorChang, Min-Kuanen_US
dc.date.accessioned2020-10-05T02:00:32Z-
dc.date.available2020-10-05T02:00:32Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-4300-2en_US
dc.identifier.issn1058-6393en_US
dc.identifier.urihttp://hdl.handle.net/11536/155064-
dc.description.abstractIn this paper, we consider a joint design of the user clustering and content caching in the cache-enabled heterogenous network (HetNet) in which users in the network have distinct content preferences. The joint clustering and caching in the HetNet relies on multitude of factors, such as channel gains in all links, which may not be fully known in practice. Besides, clustering and caching may exhibit a fundamental tradeoff between the content hit probability and the spectral efficiency. We are therefore motivated to tackle this challenging problem by the deep reinforcement learning (DRL). In particular, the deep deterministic policy gradient (DDPG) algorithm is employed to manage the dynamics of clustering and caching in the HetNet with a sizable action space. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.en_US
dc.language.isoen_USen_US
dc.titleJoint User Clustering and Content Caching with Heterogeneous User Content Preferencesen_US
dc.typeProceedings Paperen_US
dc.identifier.journalCONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERSen_US
dc.citation.spage1314en_US
dc.citation.epage1317en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000544249200251en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper