A reliable cooperative spectrum detection scheme in cognitive radio networks

Abstract

Reliable spectrum detection of the primary user (PU) performs an important role in the cognitive radio network since it's the foundation of other operations. Spectrum sensing and cognitive signal recognition are two key tasks in the development of cognitive radio (CR) technology in both commercial and military applications. However, when the CR terminals receiving signals have little knowledge about the channel or signal types, these two tasks will become much more difficult. In this paper, we propose a reliable cooperative spectrum detection scheme, which combines the cooperative spectrum sensing with distributed cognitive signal recognition. A novel improved cooperative sensing algorithm is achieved by using a credibility weight factor and the "tug-of-war" rule, which is based on the double threshold detection and Dempster---Shafer theory, to determine whether the PU signals exist. In this scheme, cognitive signal recognition can be used to identify the signal type when the PU signal is present. During the cognitive signal recognition processing, the CR terminals make local classification of the received signals by using Daubechies5 wavelet transform and Fractional Fourier Transform, and send their recognition results to the globe decision making center. A distributed processing uses these cognitive terminals' local results to make final decisions under the Maximum Likelihood estimation algorithm. Simulation results show that the proposed method can achieve good sensing probability and recognition accuracy under the Additive White Gaussian Noise channel.