Cognitive radio network as sensors: Low signal-to-noise ratio collaborative spectrum sensing

This paper propose a function of covariance matrix based spectrum sensing approach for cognitive radio systems. The statistical covariance of signal and noise are usually different, so a binary hypothesis test on covariance matrix is employed to determine the existence of primary user. Collaborative sensing scenario is introduced for the proposed algorithm, in which each sensor only needs limited sample data for calculation and sends mediate result to fusion center. A performance comparison among different rational functions is provided, which shows different functions in this algorithm may have similar or distinct performance. So it is important to choose an appropriate function. The proposed algorithm has a reliable performance in very low signal-to-noise ratio (SNR) condition, and outperforms the Estimator-Correlator (EC) approach.