User Preference Learning Based Edge Caching for Fog-RAN
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In this paper, the edge caching problem in fog radio access networks (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal edge caching policy. We consider content popularity in terms of time and space from the perspective of regional users. Taking into account that users request the contents they are interested in, 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 computational complexity, but it also can track the content popularity with spatial and temporal popularity dynamics in time without delay. 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. Furthermore, to implement our proposed edge caching policy, we design two learning based edge caching architectures for F-RAN, which have the capability of flexibly setting the monitoring cycle and is effective in various edge caching scenarios. 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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