Prioritized Spectrum Sensing in Cognitive Radio Based on Spatiotemporal Statistical Fusion

In this paper, a novel statistics-driven spectrum sensing algorithm is developed for improving spectrum sensing efficiency in the media access control (MAC) layer of cognitive radio (CR) systems. The proposed algorithm aims to achieve higher spectrum sensing efficiency and spectrum access opportunity by prioritizing channels for fine sensing based on the statistical likelihood of channel availability. The sensing priority is obtained by jointly exploiting the long-term spatiotemporal statistics recorded from the historical result of fine sensing, the short-term statistical information of channel condition obtained from a small-scale observation window, and the instantaneous statistical information obtained from fast sensing. Simulation results show that the proposed prioritization algorithm can achieve improved data transmission rates and reduced missed spectrum access opportunities when compared to the conventional nonprioritization spectrum sensing approach for situations where cooperative spectrum sensing is not suitable.