University of Oulu

Y. Jiang, M. Ma, M. Bennis, F. Zheng and X. You, "User Preference Learning-Based Edge Caching for Fog Radio Access Network," in IEEE Transactions on Communications, vol. 67, no. 2, pp. 1268-1283, Feb. 2019. doi: 10.1109/TCOMM.2018.2880482

User preference learning-based edge caching for fog radio access network

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Author: Jiang, Yanxiang1,2,3; Ma, Miaoli1; Bennis, Mehdi4;
Organizations: 1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
3Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
4Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
5School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202003117807
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-03-11
Description:

Abnstract

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 67
Issue: 2
Pages: 1268 - 1283
DOI: 10.1109/TCOMM.2018.2880482
OADOI: https://oadoi.org/10.1109/TCOMM.2018.2880482
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the Natural Science Foundation of Jiangsu Province under grant BK20181264, the Research Fund of the State Key Laboratory of Integrated Services Networks (Xidian University) under grant ISN19-10, the Research Fund of the Key Laboratory of Wireless Sensor Network & Communication (Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences) under grant 2017002, the National Basic Research Program of China (973 Program) under grant 2012CB316004, and the U.K. Engineering and Physical Sciences Research Council under Grant EP/K040685/2.
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