Y. Jiang, M. Ma, M. Bennis, F. Zheng and X. You, "A Novel Caching Policy with Content Popularity Prediction and User Preference Learning in Fog-RAN," 2017 IEEE Globecom Workshops (GC Wkshps), Singapore, 2017, pp. 1-6. doi: 10.1109/GLOCOMW.2017.8269166
A novel caching policy with content popularity prediction and user preference learning in Fog-RAN
|Author:||Jiang, Yanxiang1; Ma, Miaoli1; Bennis, Mehdi2;|
1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
3Department of Electronic Engineering, University of York, York YO10 5DD, U.K
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018073033117
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2018-07-30
In this paper, the edge caching problem in fog radio access networks (F-RAN) is investigated. By maximizing the cache hit rate, we formulate the edge caching optimization problem to find the optimal edge caching policy. Considering that users prefer to request the contents they are interested in, we propose to implement online content popularity prediction by leveraging the content features and user preferences, and offline user preference learning by using the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) algorithm and the "Online Gradient Descent" (OGD) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low computational complexity, but also can track the popularity changes in time without delay. Simulation results show that the cache hit rate of our proposed policy approaches the optimal performance and is superior to those of the traditional policies.
2017 IEEE Globecom Workshops (GC Wkshps)
IEEE Globecom Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Ericsson and SEU Cooperation Project (8504000335), the National 863 Project (2015AA01A709), the National Basic Research Program of China (973 Program 2012CB316004), the Natural Science Foundation of China (61521061), and the UK Engineering and Physical Sciences Research Council under Grant EP/K040685/2.
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