In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. Initial features of local users and contents are generated by incorporating neighbor information with self information. Then, dual-channel neural network (DCNN) model is introduced to learn the hidden representations by producing deep latent features from initial features. For mobile users, the content popularity is predicted via user preference learning. In order to distinguish regional variations of content popularity, clustered federated learning (CFL) is employed, which enables fog access points (F-APs) with similar regional types to benefit from one another and provides a more specialized DCNN model for each F-AP. Simulation results show that our proposed policy achieves significant performance improvement over the traditional policies.
翻译:在本文中,对雾无线电接入网络(F-RANs)中的内容受欢迎程度预测问题进行了调查;根据分组化联合学习,我们建议采用新的流动意识受欢迎程度预测政策,将当地用户和移动用户的内容受欢迎程度纳入其中;对当地用户而言,通过了解当地用户和内容的隐蔽表现来预测内容受欢迎程度;当地用户的初始特征和内容是通过将邻居信息与自我信息相结合而生成的;然后,采用双通道神经网络模式,通过从初始特征中生成深层潜伏特征来了解隐藏的表现形式;对移动用户而言,通过用户偏好学习来预测内容受欢迎程度;为区分区域内容受欢迎程度的差异,采用分组化联合学习(CFL),使类似区域类型的雾接入点能够相互受益,并为每个F-AP提供更加专业化的DCNNN模式。模拟结果表明,我们提出的政策在传统政策上取得了显著的业绩改进。