The Spectrum Sensing Algorithm Based AdaBoost in Cognitive Radio

To solve the low detection rate of the primary user in the cognitive radio environment, we propose a spectrum sensing method based on AdaBoost in the case of low SNR. In this paper, a set of received signal spectrum features are first calculated and extracted the discriminant feature vector as training samples and testing samples for classification. Finally, we utilize the trained AdaBoost to detect the primary user. Test result shows that the proposed algorithm is not affected by uncertainty factors of noise and has high performance to classification detection compared with ANN, SVM and maximum-minimum eigenvalue (MME).