Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks
This work addresses caching efficiency in fog radio access networks, which is incremental as it builds on existing RL methods with specific improvements for this domain.
The paper tackles the distributed edge caching problem in fog radio access networks by proposing a reinforcement learning-based method to optimize caching policies, with simulation results showing superior performance compared to traditional methods.
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.