A Spectrum Sensing Based on Support Vector Machine Algorithm in the Building Indoors Environment

Spectrum sensing performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a primary users sensing approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, primary user signal has been detected. Simulations indicate that the overall success rate is above 90.2% when SNR is equal to-5dB and 80.1% in-15dB. 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 in building indoor environment.