Wideband Spectrum Sensing Based on Bidirectional Decision of Normalized Spectrum for Cognitive Radio Networks

Abstract

Cognitive radio (CR) is considered to be an effective approach to eliminate the dilemma of spectrum shortage. To meet the ever-increasing demands of instant and accurate spectrum sensing in CR with wideband and multi-frequency-slots, a novel wideband spectrum sensing algorithm based on bidirectional decision of normalized spectrum (BDNP) is proposed in this paper. The proposed algorithm takes the normalized power spectrum within the frequency slot as the detection statistics, and finds out all of the occupied frequency slots in the range of the target bandwidth by searching forward and backward in sequence. The asymptotic normality and independence of Fourier transform is proved firstly, and based on which the false alarm probability of single decision is derived. Additionally, the closed-form expression of decision threshold is obtained by using Neyman-Pearson criterion. Theoretical analysis and simulation results show that the BDNP algorithm can accurately identify occupied frequency slots, which provides the base of avoiding interference to the primary users. Furthermore, comparing with the spectrum sensing algorithm based on conventional spectral estimation (CSE), BDNP algorithm can effectively overcome noise uncertainty in spectrum sensing.