Introduction - If you have any usage issues, please Google them yourself
This paper proposes a novel, highly effective spectrum
sensing algorithm for cognitive radio and white space
applications. The proposed spectral covariance sensing (SCS)
algorithm exploits the different statistical correlations of the received
signal and noise in the frequency domain. Test statistics are
computed the covariance matrix of a partial spectrogram
and compared with a decision threshold to determine whether a
primary signal or arbitrary type is present or not. This detector is
analyzed theoretically and verified through realistic open-source
simulations using actual digital television signals captured in the
US. Compared to the state of the art in the literature, SCS
improves sensitivity by 3 dB for the same dwell time, which is a
very significant improvement for this application. Further, it is
shown that SCS is highly robust to noise uncertainty, whereas
many other spectrum sensors are not.