Exploiting Predictability in Dynamic Network Communication for Power-Efficient Data Transmission in LTE Radio Systems

In embedded systems powered by batteries, power is undoubtedly a critical resource making power management an important topic in the design phase. Even though power management is a heavily researched topic, most approaches focus on improving the way the power manager reacts to outside control events. In this paper, we propose techniques that not only react but rather try to predict these outside control events in advance, thus, broadening the capabilities of any employed power manager by allowing for superior transition decisions and even saving redundant calculations. We present results on employing a predictive power management system that couples a classic dynamic power manager with a machine learning subsystem in the context of a mobile device in a Long Term Evolution (LTE) system, with emphasis on evaluating the potential of saving power as well as the handling of the induced prediction uncertainty. First, we examine the LTE communication protocol and showcase certain control data that has to be received periodically, but may contain no information for the receiver. Finally, we show a proof-of-concept based on real LTE traces and hardware simulation, that prediction of this information can be leveraged to allow for a far superior decision process compared to a non-predicting system. Here, we achieve a theoretical best case power saving of 15 % for an idealized prediction with 100 % accuracy and no additional power consumption.