Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis

Spectrum sensing is a fundamental component of cognitive radio (CR). How to promptly sense the presence of primary users (PUs) is a key issue to a CR network. The time requirement is critical in that violating it will cause harmful interference to the PU, leading to a system-wide failure. The motivation of this paper is to provide an effective spectrum sensing method to detect PUs as soon as possible. In the language of streaming-based real-time data processing, short time means small data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmid´s concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV (DTV) signal, show that this proposed method can operate either using smaller data or working under a lower signal-to-noise ratio (SNR) environment.