Automatic modulation classification for cognitive radios using cumulants based on fractional lower order statistics

Automatic modulation classification (AMC) finds various applications in cognitive radios. This paper presents a method for the automatic classification using cumulants derived using fractional lower order statistics. The performance of the classifier is presented in the form of probability of correct classification under noisy and fading conditions. Unlike many of the conventional methods, the proposed method does not require a priori knowledge of signal parameters. The proposed method is also more robust to different noises. Simulation results show that the proposed method can achieve better classification accuracy when compared to conventional cumulant based AMC method, in various impulsive noise conditions.