SECOND-ORDER STATISTICAL APPROACH FOR DIGITAL MODULATION SCHEME CLASSIFICATION IN COGNITIVE RADIO USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOR CLASSIFIER

Cognitive radio systems require detection of different signals for communication. In this study, an approach for multiclass signal classification based on second-order statistical feature is proposed. The proposed system is designed to recognize three different digital modulation schemes such as PAM, 32QAM and 64QAM. The signal classification is achieved by extracting the 2nd order cumulants of the real and imaginary part of the complex envelope. These second-order statistical features are given to multiclass Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier for classification. The modulated signals are passed through an Additive White Gaussian Noise (AWGN) channel before feature extraction. The performance evaluation of the system is carried using 400 generated signals. Experimental results show that the proposed method produces an accurate classification rate in the range 65%-89 % for SVM classifier and 65-68 % for KNN classifier