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This paper proposes a novel, highly effective spectrum sensing algorithm for cognitive radio and whitespace 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 from 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.
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