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Methods for accurate prediction of radio signal quality parameters arecrucial for optimization of mobile networks, and a necessity for futureautonomous driving solutions. The power-distance relation of current empiricalmodels struggles with describing the specific local geo-statistics thatinfluence signal quality parameters. The use of empirical models commonlyresults in an over- or under-estimation of the signal quality parameters andrequire additional calibration studies. In this paper, we present a novelmodel-aided deep learning approach for path loss prediction, which implicitlyextracts radio propagation characteristics from top-view geographical images ofthe receiver location. In a comprehensive evaluation campaign, we apply theproposed method on an extensive real-world data set consisting of fivedifferent scenarios and more than 125.000 individual measurements. It is foundthat 1) the novel approach reduces the average prediction error by up to 53% incomparison to ray-tracing techniques, 2) A distance of 250-300 meters spannedby the images offer the necessary level of detail, 3) Predictions with aroot-mean-squared error of approximately 6 dB is achieved across inherentlydifferent data sources.

Deep Learning-based Signal Strength Prediction Using Geographical Images and Expert Knowledge