Robust, Non-Gaussian Wideband Spectrum Sensing in Cognitive Radios

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