Robust, Non-Gaussian Wideband Spectrum Sensing in Cognitive Radios

摘要: In this paper, we propose detectors (both parametric and robust) for wideband spectrum sensing in cognitive radios (CR's). The proposed are able to detect spectral activity over a wide frequency range, while assuming little knowledge about the signals of interest. detector is based on locally optimal (LO) Neyman-Pearson (NP) test assumes known non-Gaussian noise distribution. corresponding decision statistic LO NP expressed domain, allowing identify active channels within band On other hand, situations which distribution only approximately known, robust signal that immune deviations model from certain nominal estimator formulated as non-linear regression. This regression problem can be solved using fixed-point iteration algorithm at quadratic computational complexity, contrast with Newton's method would have cubic complexity order. simulation results show achieve better detection performance presence noise, compared existing under same conditions.