  Machine learning (ML) and artificial neural networks (ANNs) have been
successfully applied to simulating complex physics by learning physics models
thanks to large data. Inspired by the successes of ANNs in physics modeling, we
use deep neural networks (DNNs) to predict the radio signal strength field in
an urban environment. Our algorithm relies on samples of signal strength
collected across the prediction space and a 3D map of the environment, which
enables it to predict the scattering of radio waves through the environment.
While already extensive body of research exists in spatial signal strength
prediction, our approach differs from most existing approaches in that it does
not require the knowledge of the transmitter location, it does not require side
channel information such as attenuation and shadowing parameters, and it is the
first work, to the best of our knowledge, to use 3D maps to accomplish the task
of signal strength prediction. This algorithm is developed for the purpose of
placement optimization of a UAV or mobile robot to maximize the signal strength
to or from a stationary transceiver but it also holds relevance to dynamic
spectrum access networks, cellular coverage design, power control algorithms,
etc.
