Compressive wideband spectrum sensing using high-order statistics for cognitive radio

Fast and accurate wideband spectrum sensing faces considerable technical challenges in cognitive radio network (CRN). In this paper, a high-order statistics (HOS)-based wideband spectrum sensing (HOS-WSS) scheme with compressive measurements is proposed. Different from traditional spectrum sensing schemes based on compressed sensing (CS) requiring the signal recovery, HOS-WSS scheme resorts to HOS directly fed by compressive measurements for detecting the licensed wideband spectrum, which can significantly reduce the computational complexity and improve the sensing agility. Both theoretical analyses and simulation results show that the proposed algorithm has lower computational complexity and is more robust to the noise uncertainty compared to the HOS-WSS scheme with generalized orthogonal matching pursuit (GOMP) reconstruction algorithm and HOS-based scheme with Nyquist samples.