Cognitive Radios: Discriminant Analysis for Automatic Signal Detection in Measured Power Spectra

Signal detection of primary users for cognitive radios enables spectrum use agility. In normal operation conditions, the sensed spectrum is nonflat, i.e., the power spectrum is not constant. A novel method proposes the segmentation of the measured spectra into regions where the flatness condition is approximately valid. As a result, an automatic detection of the significant spectral components together with an estimate of the magnitude of the spectral component and a measure of the quality of classification becomes available. In this paper, we optimize the methodology for signal detection in cognitive radios such that the probability that a spectral component was incorrectly classified is iteratively reduced. Simulation and measurement results show the advantages of the presented technique in different types of spectra.