Novel Semi-Blind Spectrum Sensing in Cognitive Radio Networks with Fourth-Order Statistics

Abstract

Cognitive radio (CR) system is able to exploit the bands allocated to licensed or primary users when they are not being used. Spectrum sensing is a key element of a working CR system. In this paper, enhanced algorithms are proposed for semi-blind spectrum sensing in CR networks using fourth-order statistics of the received primary user's signal. The proposed statistic will have a value equal or close to 3 when only Gaussian noise samples exist in the received signal. This estimate is used to differentiate between the presence or absence of the primary user by comparing with a predefined threshold. Using the Neyman-Pearson criterion, an optimized threshold is established and an analytical expression for the upper bound on the average probability of miss-detection $$(P_{m,avg})$$(Pm,avg) is also derived. The proposed algorithm clearly outperforms the energy detection (ED) method over the low-SNR range of $$-15$$-15 to $$12$$12 dB. For $$P_{m,avg} = 10^{-2}$$Pm,avg=10-2 , e.g., the proposed scheme outperforms the ED method by 1.5 dB for the case of a single user. Moreover, the proposed algorithm has been extended to the cooperative spectrum sensing model. Simulation results show that the proposed scheme significantly outperforms the ED method in cooperative spectrum sensing scenarios.