Probability-based periodic spectrum sensing during secondary communication

Spectrum sensing in cognitive radio (CR) typically assumes that the primary user appears only at the beginning of the sensing block. In this paper, we first establish a probability model regarding the appearance of the primary user at any sample of a CR user frame by utilizing the statistical characteristics of the licensed channel occupancy. While conventional spectrum sensing schemes allocate the same weight to each sample, we vary the weight for each sample based on the probability of the presence of the primary user at the corresponding sample and show that such a probability-based spectrum sensing scheme has nearly optimal performance. Based on the assumption that the idle duration of the licensed channel is exponentially distributed, we further investigate how the probability model on the primary user appearance varies from frame to frame in periodic spectrum sensing and show that both the conventional fixed weight and the probability-based dynamic weight energy detection schemes converge to their respective stable detection probability.