Primary User Signal Type Recognition Algorithm of Cognitive Radio Networks based on Active Learning in Building Indoors

摘要

Primary user signal modulation type recognition performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a method based on active learning and support vector machine (SVM) for the primary user signal modulation type recognition is proposed in low signal to noise ratio. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis, and the training samples and testing samples are formed for classification. Then, active learning algorithm is applied to obtain samples improved classification through a number of iterations, and SVM is formed. Finally, the formed SVM is utilized to recognize the primary user signal modulation type. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm on primary user signal modulation type recognition in building indoor environment.