  Methods for accurate prediction of radio signal quality parameters are
crucial for optimization of mobile networks, and a necessity for future
autonomous driving solutions. The power-distance relation of current empirical
models struggles with describing the specific local geo-statistics that
influence signal quality parameters. The use of empirical models commonly
results in an over- or under-estimation of the signal quality parameters and
require additional calibration studies. In this paper, we present a novel
model-aided deep learning approach for path loss prediction, which implicitly
extracts radio propagation characteristics from top-view geographical images of
the receiver location. In a comprehensive evaluation campaign, we apply the
proposed method on an extensive real-world data set consisting of five
different scenarios and more than 125.000 individual measurements. It is found
that 1) the novel approach reduces the average prediction error by up to 53% in
comparison to ray-tracing techniques, 2) A distance of 250-300 meters spanned
by the images offer the necessary level of detail, 3) Predictions with a
root-mean-squared error of approximately 6 dB is achieved across inherently
different data sources.
