Adaptive Power Optimization for Mobile Traffic Based on Machine Learning

As 5G high-speed mobile communications developing rapidly, services and contents that people get from the Internet have been enriched significantly, which necessitates the user equipment (UE) to have least power consumption, reduced latency, enhanced lifetime and better QoS. However, the tail energy of LTE interface on UE leads to low energy efficiency which is caused by applying fixed Radio Resource Control (RRC) inactivity timer. In this paper, we propose a novel approach to eliminate the tail whenever possible and improve the user equipment power efficiency. We design a self-adaptive tool to optimize the LTE RRC inactivity timer for individual users based on user model. Firstly, The tool collects runtime network information from cellular networks and uses machine learning method to predict the session length. Then it adjusts inactivity timer dynamically based on the predicted session length. In addition, we propose an enhanced RRC protocol to support our proposed tool. To demonstrate the effectiveness of our energy-saving tool, we applied it on commercial off-the-shelf phones. Simulation results show the proposed tool can reduce the energy consumption of smart-phone by 27-33.5%.