Spectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the cognitive radio is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we study a spectrum sensing technique based on spectral correlation for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that this sensing technique is asymptotically equivalent to the likelihood ratio test (LRT) at very low SNR, but with less computational complexity. That is, the spectral correlation-based detector is asymptotically optimal according to the Neyman-Pearson criterion. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR levels as low as -20 dB.

Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we present a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that according to the Neyman-Pearson criterion, this spectra correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR as low as -20 dB.

A comparison is made between the FCC geo-location and spectrum sensing requirements in the TV white space. Background material on propagation models and conversion between electromagnetic field strength and receive power is provided. The geo-location and spectrum sensing requirements are both converted to field strength where they can be compared, and a typical TV broadcast example is provided to allow comparisons in terms of the physical distances of the protection contour, the geo-location contour and the spectrum sensing contour. Observations are made about the FCC tests of the TV white space prototype devices and the interpretation of those results.