  In this paper we propose a highly efficient and very accurate deep learning
method for estimating the propagation pathloss from a point $x$ (transmitter
location) to any point $y$ on a planar domain. For applications such as
user-cell site association and device-to-device link scheduling, an accurate
knowledge of the pathloss function for all pairs of transmitter-receiver
locations is very important. Commonly used statistical models approximate the
pathloss as a decaying function of the distance between transmitter and
receiver. However, in realistic propagation environments characterized by the
presence of buildings, street canyons, and objects at different heights, such
radial-symmetric functions yield very misleading results. In this paper we show
that properly designed and trained deep neural networks are able to learn how
to estimate the pathloss function, given an urban environment, in a very
accurate and computationally efficient manner. Our proposed method, termed
RadioUNet, learns from a physical simulation dataset, and generates pathloss
estimations that are very close to the simulations, but are much faster to
compute for real-time applications. Moreover, we propose methods for
transferring what was learned from simulations to real-life. Numerical results
show that our method significantly outperforms previously proposed methods.
