Clustering-Based Compressive Wide-Band Spectrum Sensing in Cognitive Radio Network

Spectrum detection technology is one of the key technologies in cognitive radio network (CRN), and its primary task is to identify the existence of spectrum holes and the appearance of authorized users. In order to meet the hard real-time and high reliable requirements of the spectrum detection in CRN, this paper presents a novel wide-band spectrum sensing algorithm, called clustering-based joint compressive sensing(C-JCS), which combines hierarchical data-fusion idea with jointly compressive reconstruction technology. To validate the efficiency and effectiveness, we compare the C-JCS with independent compressive sensing (ICS) and joint compressive sensing (JCS) in the detection probability, false-alarm probability and algorithm execution time under the circumstance of different SNR and compression ratio. The simulation results show that the C-JCS can sense the wide-band spectrum with high accuracy in time, so as to meet the requirements of the spectrum sensing in CRN.