雾无线接入网中基于Q-learning的协作式内容缓存策略

为了提高雾无线接入网（Fog-Radio Access Networks，F-RAN）的边缘缓存效率，提出一种基于用户偏好预测和内容流行度预测的协作式内容缓存策略。首先，利用主题模型中隐含狄利克雷分布（Latent Dirichlet Allocation，LDA）模型动态的预测用户偏好；其次，利用网络中不同设备之间的拓扑关系和已预测的用户偏好以在线的方式预测内容流行度的变化，然后再结合基站之间的相关度，以减少缓存内容文件的重复率；最后，以最大化缓存命中率为目标，利用强化学习中的Q-learning算法获得了最优的内容缓存策略。仿真结果表明，与其他内容缓存策略相比，该内容缓存策略能有效的提高缓存命中率。 

Q-Learning Based Cooperative Content Caching Strategy for Fog Radio Access Networks

In order to improve the edge caching efficiency of Fog-Radio Access Network (F-RAN), a collaborative content caching strategy based on user preference prediction and content popularity prediction is proposed in this paper. Firstly, the Latent Dirichlet Allocation (LDA) model in the subject model is used to dynamically predict user preferences. Secondly, the topological relationships between different devices in the network and the obtained predicted user preferences are utilized to predict the changes of content popularity in an online fashion.Combining with correlation between the base station to reduce the repetition rate of cache content files. Finally, with the aim of maximizing the cache hit ratio, the Q-learning algorithm in reinforcement learning is used to obtain the optimal content caching strategy. Simulation results show that the proposed content caching strategy can effectively improve the cache hit ratio compared with existing content caching strategies.