Joint content popularity prediction and content delivery policy for cache-enabled D2D networks: A deep reinforcement learning approach

Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, Zhu Han

科研成果: 书/报告/会议事项章节会议稿件同行评审

15 引用 (Scopus)

摘要

Compared with traditional device-to-device (D2D) communication networks, the users in the cache-enabled D2D communication networks can easily obtain their requested contents from the nearby users, and reduce the backhaul costs. In this paper, we investigate the caching strategy for the cache-enabled D2D communication networks, with the consideration of caching placement and caching delivery. The content popularity and user mobility are predicted by a machine learning approach of echo state networks (ESNs) in order to determine which content to cache and where to cache. Furthermore, a deep Q-learning network (DQN) algorithm is proposed to optimize the content delivery problem, with taking the delays and energy consumption into consideration. Simulation results show that the content hit rate and the traffic offloading can be remarkably improved with the proposed approach, compared to the random caching strategy.

源语言英语
主期刊名2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
609-613
页数5
ISBN(电子版)9781728112954
DOI
出版状态已出版 - 2 7月 2018
活动2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, 美国
期限: 26 11月 201829 11月 2018

出版系列

姓名2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

会议

会议2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
国家/地区美国
Anaheim
时期26/11/1829/11/18

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