Research and Performance Analysis of Spectrum Sensing Algorithm Based on the Power Spectral Density Segment Cancellation

In a valid cognitive radio system, the requirement for real-time spectrum sensing in the case of lacking priori information of primary user, fading channel and dynamically varying noise level, indeed poses a major challenge to the classical spectrum sensing algorithms. In this paper, a novel spectrum sensing algorithm based on the Power spectral density Segment Cancellation (PSC) is proposed. It makes use of asymptotic normality and independence of Fourier transform to get the stochastic properties of Power Spectral Density (PSD). The proposed algorithm takes the ratio of some PSD lines to all of them as the detection statistics to detect signals. The mathematical expression for probabilities of false alarm and correct detection in different channel models is derived. In accordance with the Neyman-Pearson criteria, the closed-form expression of decision threshold is calculated. The theoretical analysis and simulation results show that the PSC algorithm is robust to noise uncertainty, and spectrum sensing performance does not vary with the ambient noise level of secondary users when Signal to Noise Ratio (SNR) is fixed. Meanwhile, the PSC algorithm could offer high probability of detection at low probability of false alarm for a wide range of the SNR in the white Gaussian noise and flat slow fading channel. The PSC spectrum sensing algorithm has low computational complexity, which can be completed in a micro-seconds duration.