  Radio maps provide metrics such as power spectral density for every location
in a geographic area and find numerous applications such as UAV communications,
interference control, spectrum management, resource allocation, and network
planning to name a few. Radio maps are constructed from measurements collected
by spectrum sensors distributed across space. Since radio maps are complicated
functions of the spatial coordinates due to the nature of electromagnetic wave
propagation, model-free approaches are strongly motivated. Nevertheless, all
existing schemes for radio occupancy map estimation rely on interpolation
algorithms unable to learn from experience. In contrast, this paper proposes a
novel approach in which the spatial structure of propagation phenomena such as
shadowing is learned beforehand from a data set with measurements in other
environments. Relative to existing schemes, a significantly smaller number of
measurements is therefore required to estimate a map with a prescribed
accuracy. As an additional novelty, this is also the first work to estimate
radio occupancy maps using deep neural networks. Specifically, a fully
convolutional deep completion autoencoder architecture is developed to
effectively exploit the manifold structure of this class of maps.
