# Adaptive Predictive Power Management for Mobile LTE Devices

## TL;DR

This paper introduces proactive machine learning algorithms to improve power management in LTE mobile devices, achieving up to 17% energy savings by predicting transmission inactivity periods.

## Contribution

It presents and compares supervised and reinforcement learning approaches for predictive power management in LTE devices, a novel proactive strategy in this context.

## Key findings

- Achieved up to 17% energy savings.
- Compared effectiveness of supervised and reinforcement learning.
- Demonstrated the feasibility of proactive power management.

## Abstract

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G standards. In addition to improving the power efficiency of components through structural and technological advances, optimizing the energy efficiency through improved dynamic power management is an integral part in contemporary hardware design. Most techniques targeting mobile devices proposed so far, however, are purely reactive in powering down and up system components. Promising approaches extend this, by predicting and using information from the environment and the communication protocol to take proactive decisions. In this paper, we propose and compare two proactive algorithmic approaches for light-weight machine learning to predict the control information needed to allow a mobile device to go to sleep states more often, e.g., in time slots of transmission inactivity in a cell. The first approach is based on supervised learning, the second one based on reinforcement learning. As the implementation of learning techniques also creates energy and resource costs, both approaches are carefully evaluated not only in terms of prediction accuracy, but also overall energy savings. Using the presented technique, we observe achievable energy savings of up to 17%.