Distributed spectrum sensing of correlated observations in cognitive radio networks

Abstract

In this paper, Collaborative Spectrum Sensing (CSS) as one of the most efficient sensing approaches in Cognitive Radio Networks (CRNs) is investigated when the Secondary Users (SUs) observations are assumed to be correlated. A novel soft decision rule based on the covariance matrix of the SUs observations is proposed. By using the proposed scheme, we derive two Generalized Likelihood Ratio (GLR) detectors and then, we obtain the closed-form expressions for the detection and false-alarm probabilities. The proposed collaborative sensing method can control the available trade-off between efficient spectrum usage and more accurate spectrum sensing, which is not possible in the other counterpart collaborative sensing methods based on the soft decision rule. In order to have the best performance in the terms of spectral efficiency, power efficiency and spectrum sensing, we study the problem of designing the fusion parameter, the decision threshold and the number of SUs to maximize power efficiency and spectrum usage efficiency under the constraint that the Primary User (PU) is sufficiently protected. Finally, we provide the computer simulations to verify the validity of the obtained results.