Abstract
The rapid development of wireless communication technology and smart devices has made the traditional cloud-based Internet of Things architecture unable to meet the stringent requirements of 5G mobile communication networks. In order to realize high-reliability and low-latency communication for 5G, F-RANs have become a potential evolution path. However, F-RANs still face several challenges regrading infrastructure, network traffic, and caching mechanism. From the point of view of integrating AI, F-RANs, and cloud, this article proposes intelligent traffic prediction and cognitive caching toward IF-RANs. First, a traffic flow prediction algorithm is developed that is based on LSTM with an attention mechanism. The algorithm can effectively predict real-time traffic of different data types. Then, for caching policy, cognitive caching based on LSTM and collaborative filtering is proposed to reduce the total communication delay. The experimental results demonstrate the effectiveness of the proposed IF-RANs in improving the accuracy of real-time prediction and reducing communication latency.