Sequential spectrum sensing based on higher-order statistics for cognitive radios

Abstract

Spectrum sensing is a crucial technique used to discover available bands that are not occupied by primary users in cognitive networks (CNs). With good sensing capability in terms of low probability of a miss occurrence, secondary users can effectively recycle the spectrum resource without disturbing active primary users. With low probability of a false alarm occurrence, spectral utilization may be relatively simple spectrum sensing technique. In practice, a cognitive radio (CR) receiver has to operate at low signal-to-noise ratio (SNR) regimes because of channel fades and noise. Therefore, low SNR inevitably degrades the performance of ED dramatically. In this paper, a sequential test detector based on higher-order statistics (HOS) is investigated to conduct effective spectral sensing, especially in low-SNR environments. By taking advantage of cumulant statistics, spectrum sensing reliability can be significantly improved as Gaussian noise can be overwhelmed. Based on binary hypothesis testing, a low-complexity sequential probability ratio test (SPRT) is thus developed for effectively detecting the vacant spectrum to meet the requirements of the sensing duty cycle. Simulation results show that the proposed detector outperforms conventional ED, especially in low SNR environments.