Reinforcement learning for energy-efficient edge caching in mobile edge networks

Edge caching has become a promising application paradigm in 5G networks, which can support the explosive growth of Internet of Things (IoTs) services and applications by caching content at the edge of the mobile network to alleviate redundant traffic. In this paper, we consider the energy minimization problem in a heterogeneous network with edge caching technique. We formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming (MINLP) problem, aiming to minimize the total system energy consumption with considering the energy consumption of users, Small Base Stations (SBSs) and Macro Base Stations (MBS). We model the optimization problem as a Markov Decision Process (MDP). Then, we propose a Q-learning based method to solve the optimization problem. Simulation results show that our proposed Q-learning method can significantly reduce the total system energy consumption in different scenarios compared with other benchmark methods.