SVM-GA Based Method for Estimation of a Large Number of Primary Users in Mobile Cognitive Radio Networks

In cognitive radio networks with mobile terminals, it is not enough for spectrum sensing only to determine whether primary user (PU) occupy the spectrum band. Sometimes we also want to know more priori information, such as, the number of PUs, which can help to estimate its carrier frequency, direction of arrival, and location. In this paper, a machine learning based method is proposed to estimate a large number of primary users. In the proposed method, support vector machine (SVM) is used to achieve the number of primary users while genetic algorithm (GA) is to optimize the parameters of SVM kernel. The first class feature of SVM is the ratio of the element sum and the trace of sample covariance matrix, and the second class feature is the mean of Gerschgorin radii. The simulation results show that our proposed SVM-GA algorithm has higher accuracy than SVM.