  Shared spectrum systems facilitate spectrum allocation to unlicensed users
without harming the licensed users; they offer great promise in optimizing
spectrum utility, but their management (in particular, efficient spectrum
allocation to unlicensed users) is challenging. A significant shortcoming of
current allocation methods is that they are either done very conservatively to
ensure correctness, or are based on imperfect propagation models and/or
spectrum sensing with poor spatial granularity. This leads to poor spectrum
utilization, the fundamental objective of shared spectrum systems.
  To allocate spectrum near-optimally to secondary users in general scenarios,
we fundamentally need to have knowledge of the signal path-loss function. In
practice, however, even the best known path-loss models have unsatisfactory
accuracy, and conducting extensive surveys to gather path-loss values is
infeasible. To circumvent this challenge, we propose to learn the spectrum
allocation function directly using supervised learning techniques. We
particularly address the scenarios when the primary users' information may not
be available; for such settings, we make use of a crowdsourced sensing
architecture and use the spectrum sensor readings as features. We develop an
efficient CNN-based approach (called DeepAlloc) and address various challenges
that arise in its application to the learning the spectrum allocation function.
Via extensive large-scale simulation and a small testbed, we demonstrate the
effectiveness of our developed techniques; in particular, we observe that our
approach improves the accuracy of standard learning techniques and prior work
by up to 60%.
