  Energy-aware system design is an important optimization task for static and
mobile Internet of Things (IoT)-based sensor nodes, especially for highly
resource-constrained vehicles such as mobile robotic systems. For 4G/5G-based
cellular communication systems, the effective transmission power of uplink data
transmissions is of crucial importance for the overall system power
consumption. Unfortunately, this information is usually hidden within
off-the-shelf modems and mobile handsets and can therefore not be exploited for
enabling green communication. Moreover, the dynamic transmission power control
behavior of the mobile device is not even explicitly modeled in most of the
established simulation frameworks. In this paper, we present a novel machine
learning-based approach for forecasting the resulting uplink transmission power
used for data transmissions based on the available passive network quality
indicators and application-level information. The model is derived from
comprehensive field measurements of drive tests performed in a public cellular
network and can be parameterized for integrating all measurements a given
target platform is able to provide into the prediction process. In a comparison
of three different machine learning methods, Random-Forest models thoroughly
performed best with a mean average error of 3.166 dB. As the absolute sum of
errors converges towards zero and falls below 1 dB after 28 predictions in
average, the approach is well-suited for long-term power estimations.
