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In this paper, the distributed edge caching problem in fog radio accessnetworks (F-RANs) is investigated. By considering the unknown spatio-temporalcontent popularity and user preference, a user request model based on hiddenMarkov process is proposed to characterize the fluctuant spatio-temporaltraffic demands in F-RANs. Then, the Q-learning method based on thereinforcement learning (RL) framework is put forth to seek the optimal cachingpolicy in a distributed manner, which enables fog access points (F-APs) tolearn and track the potential dynamic process without extra communicationscost. Furthermore, we propose a more efficient Q-learning method with valuefunction approximation (Q-VFA-learning) to reduce complexity and accelerateconvergence. Simulation results show that the performance of our proposedmethod is superior to those of the traditional methods.

Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks