Deep reinforcement learning for energy-efficient edge caching in mobile edge networks

Edge caching has emerged as a promising application paradigm in 5G networks, and by building edge networks to cache content, it can alleviate the traffic load brought about by the rapid growth of Internet of Things (IoT) services and applications. Due to the limitations of Edge Servers (ESs) and a large number of user demands, how to make the decision and utilize the resources of ESs are significant. In this paper, we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming (MINLP). To address the optimization problem, a Deep Q-Network (DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load. In addition, the DQN-based method can effectively solve the limitation of traditional reinforcement learning (RL) in complex scenarios. Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods, and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.