  Too high sampling rate is the bottleneck to wideband spectrum sensing for
cognitive radio (CR). As the survey shows that the sensed signal has a sparse
representation in frequency domain in the mass, compressed sensing (CS) can be
used to transfer the sampling burden to the digital signal processor. An analog
to information converter (AIC) can randomly sample the received signal with
sub-Nyquist rate to obtained the random measurements. Considering that the
static frequency spectrum allocation of primary radios means the bounds between
different primary radios is known in advance, here we incorporate information
of the spectrum boundaries between different primary user as a priori
information to obtain a mixed l2/l1 norm denoising operator (MNDO). In the
MNDO, the estimated power spectrum density (PSD) vector is divided into block
sections with bounds corresponding different allocated primary radios.
Different from previous standard l1-norm constraint on the whole PSD vector, a
sum of the l2 norm of each section of the PSD vector is minimized to encourage
the local grouping distribution while the sparse distribution in mass, while a
relaxed constraint is used to improve the denoising performance. Simulation
demonstrates that the proposed method outperforms standard sparse spectrum
estimation in accuracy, denoising ability, etc.
