期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 1, 页码 544-557出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2951509
关键词
Actor-critic learning; caching; deep Q-learning network; prediction; recurrent neural network (RNN)
资金
- Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation
- Shanghai Aerospace Science and Technology Innovation Fund [SAST2018045, SAST2016034, SAST2017049]
- China Fundamental Research Fund for the Central Universities [3102018QD096]
- National Natural Science Foundation of China [61671269]
- Beijing Natural Science Foundation [4191001]
- Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University [ZZ2019024]
- U.S. MURI AFOSR MURI [18RT0073]
- NSF [EARS-1839818, CNS1717454, CNS-1731424, CNS-1702850, CNS-1646607]
Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled device-to-device (D2D) communication technology is expected to relieve network pressure with the fact that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this article, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches [the echo state network (ESN) and the long short-term memory (LSTM) network] are employed to predict users' mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay and energy consumption.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据