  Spatial prediction of the radio propagation environment of a transmitter can
assist and improve various aspects of wireless networks. The majority of
research in this domain can be categorized as 'reactive' spatial prediction,
where the predictions are made based on a small set of measurements from an
active transmitter whose radio environment is to be predicted. Emerging
spectrum-sharing paradigms would benefit from 'proactive' spatial prediction of
the radio environment, where the spatial predictions must be done for a
transmitter for which no measurement has been collected.
  This paper proposes a novel, supervised deep learning-based framework,
ProSpire, that enables spectrum sharing by leveraging the idea of proactive
spatial prediction. We carefully address several challenges in ProSpire, such
as designing a framework that conveniently collects training data for learning,
performing the predictions in a fast manner, enabling operations without an
area map, and ensuring that the predictions do not lead to undesired
interference. ProSpire relies on the crowdsourcing of transmitters and
receivers during their normal operations to address some of the aforementioned
challenges. The core component of ProSpire is a deep learning-based
image-to-image translation method, which we call RSSu-net. We generate several
diverse datasets using ray tracing software and numerically evaluate ProSpire.
Our evaluations show that RSSu-net performs reasonably well in terms of signal
strength prediction, 5 dB mean absolute error, which is comparable to the
average error of other relevant methods. Importantly, due to the merits of
RSSu-net, ProSpire creates proactive boundaries around transmitters such that
they can be activated with 97% probability of not causing interference. In this
regard, the performance of RSSu-net is 19% better than that of other comparable
methods.
