Non-parametric Spectrum Sensing based on Censored Observations in Quasi-static Fading Channel for Cognitive Radio

Abstract

The detection of primary user (PU) is a challenging task for a cognitive radio user to access the spectrum opportunistically. In this paper, a spectrum sensing method based on censored observations is proposed as Censored Anderson Darling sensing (CAD). We evaluate the performance of the CAD sensing method with Monte Carlo simulations. It is shown that the proposed method outperforms the energy detection at about 6 dB gain over quasi-static fading channel at lower signal to noise ratio. Also, it gives better detection performance compared to AD sensing and OS based sensing methods which have recently been proposed in the literature.