Distributed edge caching via reinforcement learning in fog radio access networks
Lu, Liuyang; Jiang, Yanxiang; Bennis, Mehdi; Ding, Zhiguo; Zheng, Fu-Chun; You, Xiaohu (2019-06-27)
L. Lu, Y. Jiang, M. Bennis, Z. Ding, F. Zheng and X. You, "Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks," 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, 2019, pp. 1-6, https://doi.org/10.1109/VTCSpring.2019.8746321
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https://urn.fi/URN:NBN:fi-fe2020050424733
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Abstract
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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