Spectrum sensing based on fractional lower order moments for cognitive radios in α-stable distributed noise

Abstract

The traditional spectrum sensing methods based on second order statistics are in general not applicable to detecting a primary user with unknown parameters in non-Gaussian noises. This paper presents a novel spectrum sensing scheme based on fractional lower order moment (FLOM) for the detection of a primary user in non-Gaussian noise that are modeled by the α-stable distribution. The new detector does not require any a priori knowledge about the primary user (PU) signal and channels. The statistics of the proposed FLOM detector are defined in a multi-user cooperative framework and its detection and false alarm probabilities as well as deflection coefficient are analyzed for both non-fading and Rayleigh fading communication channels between the primary and secondary users. The detection performance of the proposed method versus the generalized signal-to-noise ratio, the characteristic exponent α and the number of cooperative users is also studied along with comparison to the Cauchy detector through computer simulations. Analytical and simulation results show that the proposed FLOM detector has a much better performance than the Cauchy detector in the α-stable distributed noise environment. It is also shown that multi-user cooperative sensing leads to a significantly higher probability of detection than the single user version.