期刊
IEEE TRANSACTIONS ON COMMUNICATIONS
卷 67, 期 2, 页码 1268-1283出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2018.2880482
关键词
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资金
- Natural Science Foundation of Jiangsu Province [BK20181264]
- Research Fund of the State Key Laboratory of Integrated Services Networks (Xidian University) [ISN19-10]
- Key Laboratory of Wireless Sensor Network & Communication (Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences) [2017002]
- National Basic Research Program of China (973 Program) [2012CB316004]
- U.K. Engineering and Physical Sciences Research Council [EP/K040685/2]
- EPSRC [EP/K040685/2] Funding Source: UKRI
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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