  As an enabling technique of cognitive radio (CR), compressive spectrum
sensing (CSS) based on compressive sensing (CS) can detect the spectrum
opportunities from wide frequency bands efficiently and accurately by using
sub-Nyquist sampling rate. However, the sensing performance of most existing
CSS excessively relies on the prior information such as spectrum sparsity or
noise variance. Thus, a key challenge in practical CSS is how to work
effectively even in the absence of such information. In this paper, we propose
a blind orthogonal least squares based CSS algorithm (B-OLS-CSS), which
functions properly without the requirement of prior information. Specifically,
we develop a novel blind stopping rule for the OLS algorithm based on its
probabilistic recovery condition. This innovative rule gets rid of the need of
the spectrum sparsity or noise information, but only requires the
computational-feasible mutual incoherence property of the given measurement
matrix. Our theoretical analysis indicates that the signal-to-noise ratio
required by the proposed B-OLS-CSS for achieving a certain sensing accuracy is
relaxed than that by the benchmark CSS using the OMP algorithm, which is
verified by extensive simulation results.
