Spectrum sensing and modulation classification for cognitive radios using cumulants based on fractional lower order statistics

Two key tasks in the development of cognitive radio networks in commercial and military applications are spectrum sensing and automatic modulation classification (AMC). These tasks become even more difficult when the cognitive radio receiver has no information about the channel or the modulation type. An integrated scheme which includes both these aspects is proposed in this paper. Spectrum sensing is done using cumulants derived from fractional lower order statistics. It is shown through simulations that the proposed sensing method has improved performance, especially in low SNR environments in Gaussian and non-Gaussian noise when compared with the conventional higher-order statistics (HOS) based method. The performance of the automatic modulation classifier is presented in the form of conditional probability of classification, probability of correct classification and confusion matrix under noisy and under fading conditions. Simulations in our previous work showed that the proposed method achieved better classification accuracy when compared to cumulant based AMC method in noise conditions that are highly impulsive than Gaussian. In this paper, simulations show significant improvement in the performance of AMC in the presence of AWGN and under multipath fading, for a known frequency band of interest when compared with the conventional AMC methods available.