Spectrum sensing is an essential pre-processing step for cognitive radio technology. This paper presents a novel method to detect the significant spectral components in measured nonflat spectra, and to estimate the magnitude of the spectral components. Furthermore, the probability that the spectral component was incorrectly classified is available. The algorithm is able to detect the presence or absence of signals in any kind of spectrum since no prior knowledge about the measured signal is needed. Hence, this method becomes a strong basis for a high-quality operation mode of cognitive radios. Simulation results prove the advantages of the presented technique.
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