Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue support heavy wireless services on sub-3GHz. Recently, broadband sensing becomes a hot topic with help of compressive technology, which will reduce high-speed sampling rate requirement analog-to-digital converter. This paper considers sequential sensing, where temporal correlation information between neighboring data be exploited. Different from conventional previous fused into prior knowledge in current estimation. The simulation results show that scheme can achieve 98.7% detection probability under 3.5% false alarm and performs best compared typical BPDN OMP schemes.