  Wideband spectrum sensing is a critical component of a functioning cognitive
radio system. Its major challenge is the too high sampling rate requirement.
Compressive sensing (CS) promises to be able to deal with it. Nearly all the
current CS based compressive wideband spectrum sensing methods exploit only the
frequency sparsity to perform. Motivated by the achievement of a fast and
robust detection of the wideband spectrum change, total variation mnimization
is incorporated to exploit the temporal and frequency structure information to
enhance the sparse level. As a sparser vector is obtained, the spectrum sensing
period would be shorten and sensing accuracy would be enhanced. Both
theoretical evaluation and numerical experiments can demonstrate the
performance improvement.
