Predicted mobile data offloading for mobile edge computing systems

Abstract

Mobile Edge Computing (MEC) has emerged as a promising technology to meet with the high data rate, real-time transmission, and huge computation requirements for the ever growing future wireless terminals, such as virtual reality devices, augmented reality, and the Internet of Vehicles. Due to the limitation of licensed bandwidth resources, mobile data offloading should be considered. On the other hand, WiFi AP that works on the abundant unlicensed spectrum can provide good wireless services under light-loaded areas. Therefore, in this paper we leverage WiFi AP to offload some devices from SBS. To effectively perform the offloading process, we build a multi-LSTM based deep-learning algorithm to predict the traffic of SBS. According to the prediction results, an offline mobile data offloading strategy has been proposed, which has been obtained through cross entropy method. Simulation results demonstrate the efficiency of our prediction model and offloading strategy.