  Spectrum maps, which provide RF spectrum metrics such as power spectral
density for every location in a geographic area, find numerous applications in
wireless communications such as interference control, spectrum management,
resource allocation, and network planning to name a few. Spectrum cartography
techniques construct these maps from a collection of measurements collected by
spatially distributed sensors. Due to the nature of the propagation of
electromagnetic waves, spectrum maps are complicated functions of the spatial
coordinates. For this reason, model-free approaches have been preferred.
However, all existing schemes rely on some interpolation algorithm unable to
learn from data. This work proposes a novel approach to spectrum cartography
where propagation phenomena are learned from data. The resulting algorithms can
therefore construct a spectrum map from a significantly smaller number of
measurements than existing schemes since the spatial structure of shadowing and
other phenomena is previously learned from maps in other environments. Besides
the aforementioned new paradigm, this is also the first work to perform
spectrum cartography with deep neural networks. To exploit the manifold
structure of spectrum maps, a deep network architecture is proposed based on
completion autoencoders.
