International Journal of Engineering and Technology

Spectrum sensing method is the fundamental factor when we are working with cognitive radio systems. Main aim and fundamental problem of cognitive radio is to identify weather primary users in authorized or licensed spectrum is presented or not. Paper deals with a new scheme of sensing based on the eigenvalues concept. It contain signals of covariance matrix received by the secondary users. In this method we are suggested two algorithms of sensing, one algorithm established by the maximum to minimum eigenvalue ratio. Other algorithm focused on average to minimum eigenvalue ratio. These two are done by using random matrix theories (RMT), and also these RMT are latest and also produce some accurate results. Now we calculate the ratios of distributions and probabilities of detection (Pd) and derive the probabilities of false alarm (Pfa) for the proposed algorithms, and also finding thresholds values for given Pfa. This method will improve the problem of noise uncertainty, and also performance is improved compare to energy detection when highly correlated signal is available. Paper also deals with another method is and also covariance methods. First one is statistical covariance method, it has different noise and received signal, and it is used for finding the primary users presence where there is only noise. These algorithms implemented by use of small number of received signal samples and processed to calculate the sample covariance matrix. By use of sample covariance matrix we are extracted two test statistics. Finally we compare these results and concluded that signal presence. These are used in many signal detection applications, and also do not need signal information, also noise power and channel. We did the Simulations based on two ways. First one is randomly generated signals. Other one is done by captured DTV signals taken from ATSV committee, these are broadcasting signals. These methods confirm and verifies the efficiency of the proposed methods.