CUMULANT APPROACH FOR MODULATION RECOGNITION

In this paper, cumulant approach is used in MQAM modulated signal recognition. This method uses the characteristics of higher-order cumulants and higher-order statistics to identify different kinds of MQAM signals. We focus on the modulation identification using statistical properties of MQAM signals. The reason of using this method is the ability of higher-order statistics to reflect the distribution characteristics of the constellation diagram.Thereare many benefits of using this method. This method needs only small amount of computations. It can effectively inhibit the effects of White Gaussian Noise. In many applications,the cumulants are applicable to distinguish between different amplitude or phase modulated signals like MQAM, MPSK,MASK. Differentmodulated signals have different cumulant values. Whenever we use higher order cumulants, we will get higher degree to distinguish the type of modulation scheme; the higher order cumulants require a large amount of data. A good characteristic of this method is that: the value of higher order cumulants (more than two orders), for the Gaussian noise is zero, so the use of higher-order cumulants characteristics can effectively remove the impact of the Gaussian noise.Simulation results reflectthe validity of proposed algorithm