Compressed Wideband Spectrum Sensing with Correlated Subband Occupancy in Multi-antenna Cognitive Radios

In this paper, we investigate the use of multiple antennas at the cognitive radio receiver for improved wideband spectrum sensing and exploit compressed sensing to reduce the extremely high sampling rate of wideband signal. Using a priori statistical knowledge about the non-zero coefficients of the sparse primary user signal, we propose two novel multiband compressive detectors to detect the presence of primary user signal within frequency subbands while the occupancies of these are correlated. Therefore, we model the occupancy of each subband as a binary random variable and assume subband energies are linearly combined. The simulation results evaluate the performance of the proposed compressive detectors compared to existing compressive detectors and the traditional energy detectors.