Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network

With emergence of Internet of Things (IoT), wireless traffic has grown dramatically, posing severe strain on core network and backhaul bandwidth. Proactive caching in mobile edge computing systems can not only efficiently mitigate the traffic congestion and relieve burden of backhaul but also can reduce the service latency for end devices. However, proactive caching heavily relies on the prediction accuracy of content popularity, which is typically unknown and change over time. In this paper, we propose an online proactive caching scheme based on bidirectional deep recurrent neural network (BRNN) model to predict time-series content requests and update edge caching accordingly. Specifically, on the first layer, a 1-D convolution neural network (CNN) is devised to reduce the computational costs. Then, BRNN is employed to predict time-varying requests from users. Afterward, a fully connected neural network (FCNN) is harnessed to learn and sample predicts from the BRNN. Finally, we conduct experiments based on real datasets, which demonstrate that the proposed approach can achieve considerably high prediction accuracy and significantly improve content hit rate of end devices.