A Novel Caching Policy with Content Popularity Prediction and User Preference Learning in Fog-RAN

Abstract

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.