On Detecting White Space Spectra for Spectral Scavenging in Cognitive Radios

A primary task performed by a Cognitive Radio is that of spectral estimation to locate the segments in a spectral span that contain white zones, spans that contains noise only, or grey zones, spans that contain signals with significant intervals of off-time. The identification of spectral regions containing noise only spectra, as opposed to regions containing signals with low spectral density, is surprising difficult. The estimator must deal with questions of transform length, window selection, window length, window overlap, and ensemble averaging options. This paper describes the impact of each selection option and presents the architecture of the optimal spectral estimator.