Eigenvalues detection based spectrum sensing algorithm for cognitive radio

The task of spectrum sensing is to use the data collected by the sensing nodes(wireless sensors or cognitive users)to decide whether the spectrum holes exist or not. Recently, the maximum eigenvalue detection(MED)and the smallest eigenvalue detection(SED)methods have been proposed for spectrum sensing. Both of them perform well for the correlated signals, which is usually the case in realistic applications. However, the determinations of the thresholds for both the MED and the SED are quite involved, which limits their applications in practical sensing situations in cognitive radio(CR). Using all eigenvalues of the sample covariance matrix(SCM), a new algorithm based on the eigenvalues detection(ESD)is introduced. Multivariate statistical theories are used to obtain the decision threshold. The proposed ESD method can execute spectrum sensing without the information about the primary signal and the wireless channel. Meanwhile, it keeps the same computation complexity as that of the MED and the SED methods. More importantly, the ESD method relaxes the calculation requirement of the decision threshold by using a simple closed-form expression. Simulation results verify the effectiveness of the proposed method.