  Spectrum sensing is a crucial component of opportunistic spectrum access
schemes, which aim at improving spectrum utilization by allowing for the reuse
of idle licensed spectrum. Sensing a spectral band before using it makes sure
the legitimate users are not disturbed. Since information about these users'
signals is not necessarily available, the sensor should be able to conduct
so-called blind spectrum sensing. Historically, this has not been a feature of
cyclostationarity-based algorithms. Indeed, in many application scenarios the
information required for traditional cyclostationarity detection might not be
available, hindering its practical applicability. In this work we propose two
new cyclostationary spectrum sensing algorithms that make use of the inherent
sparsity of the cyclic autocorrelation to make blind operation possible. Along
with utilizing sparse recovery methods for estimating the cyclic
autocorrelation, we take further advantage of its structure by introducing
joint sparsity as well as general structure dictionaries into the recovery
process. Furthermore, we extend a statistical test for cyclostationarity to
accommodate sparse cyclic spectra. Our numerical results demonstrate that the
new methods achieve a near constant false alarm rate behavior in contrast to
earlier approaches from the literature.
