  Spectrum sensing is a fundamental component in cognitive radio. A major
challenge in this area is the requirement of a high sampling rate in the
sensing of a wideband signal. In this paper a wideband spectrum sensing model
is presented that utilizes a sub-Nyquist sampling scheme to bring substantial
savings in terms of the sampling rate. The correlation matrix of a finite
number of noisy samples is computed and used by a subspace estimator to detect
the occupied and vacant channels of the spectrum. In contrast with common
methods, the proposedmethod does not need the knowledge of signal properties
that mitigates the uncertainty problem. We evaluate the performance of this
method by computing the probability of detecting signal occupancy in terms of
the number of samples and the SNR of randomly generated signals. The results
show a reliable detection even in low SNR and small number of samples.
