A combination of quickest detection with oracle approximating shrinkage estimation and its application to spectrum sensing in cognitive radio

Abstract

Spectrum sensing is a fundamental problem in cognitive radio. How to sense the presence of primary user promptly in order to avoid the unexpected interference is a key issue to the system. The motivation of our work is to detect the primary user signal using small size data in short time. In this paper, a quickest detection based approach is proposed for spectrum sensing. This approach employs covariance matrix estimation instead of sample covariance matrix as the first step, then the core idea of sequential detection or quickest detection is borrowed and utilized here to improve the performance of traditional eigenvalue based MME and AGM detectors. The main advantage of the proposed approach is that it requires short data to detect quickly and it works at lower SNR environments than some traditional methods. A performance comparison between the proposed approach and other traditional methods is provided, by the simulation on captured digital TV (DTV) signal. The simulation results show this proposed approach exhibits performance improvement while the threshold keeps robust.