Cyclostationary feature detection is one of the most powerful spectrum sensing techniques
used for cognitive radio (CR) systems. This is because of its robustness against noise
uncertainties. However, this technique needs high sampling rates, which is limited by the
state-of the-art analog to digital converters (ADCs), especially in wideband regime.
Comressive sensing (CS) was used by many researchers for solving this problem via
sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does
not reduce complexity considerably. This is because spectrum sensing is performed in three
steps: sensing compressed measurements, then reconstructing the Nyquist rate signal, and
finally performing cyclostationary detection (CD) on the reconstructed signal. In this paper
we suggest performing CD directly on the compressed measurements skipping the
reconstruction step which is the most complex step in CS. This can be realized by designing
the sensing matrix with constraints different from those used in the conventional CS. Results
show that performance is improved relative to applying CD on the Nyquist rate signal. This
is in addition to reduction in receiver complexity resulting from reducing sampling rates. A
detection probability of 78.7% can be achieved with only 7% of samples used by the
conventional cyclostationary detection technique that achieves a detection probability of
32.7%.