  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%.
