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In this paper, we address the problem of Received Signal Strength mapreconstruction based on location-dependent radio measurements and utilizingside knowledge about the local region; for example, city plan, terrain height,gateway position. Depending on the quantity of such prior side information, weemploy Neural Architecture Search to find an optimized Neural Network modelwith the best architecture for each of the supposed settings. We demonstratethat using additional side information enhances the final accuracy of theReceived Signal Strength map reconstruction on three datasets that correspondto three major cities, particularly in sub-areas near the gateways where largervariations of the average received signal power are typically observed.

Deep Learning with Partially Labeled Data for Radio Map Reconstruction