標題: Joint User Clustering and Content Caching with Heterogeneous User Content Preferences
作者: Chiu, Feng
Kuo, Ting-Yu
Chien, Feng-Tsun
Huang, Wan-Jen
Chang, Min-Kuan
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
公開日期: 1-Jan-2019
摘要: In 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.
URI: http://hdl.handle.net/11536/155064
ISBN: 978-1-7281-4300-2
ISSN: 1058-6393
期刊: CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS
起始頁: 1314
結束頁: 1317
Appears in Collections:Conferences Paper