In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods.


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    Titel :

    Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks


    Beteiligte:
    Lu, Liuyang (Autor:in) / Jiang, Yanxiang (Autor:in) / Bennis, Mehdi (Autor:in) / Ding, Zhiguo (Autor:in) / Zheng, Fu-Chun (Autor:in) / You, Xiaohu (Autor:in)


    Erscheinungsdatum :

    2019-04-01


    Format / Umfang :

    2406041 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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