Cooperative modulation classification of multiple signals in cognitive radio networks

Automatic Modulation Classification (AMC) is an important component in Cognitive Radio (CR) Networks. Multiuser AMC classifies the modulation schemes of simultaneous multiple unknown transmitters. In addition, cooperation among multiple CR receivers for modulation classification offers significant improvements in classification performance and overcomes the detrimental channel effects that degrades the single CR classifier performance. In this paper, a novel centralized soft-combining data fusion algorithm based on the joint probability distribution of fourth order cumulants is presented for cooperative modulation classification. Fourth order cumulants of the received signals are calculated as discriminating features for different modulation schemes at each CR node and sent to a centralized data Fusion Center (FC). The FC chooses the modulation scheme that maximizes the joint probability of the estimated cumulants. As compared to independent receiver classification, cooperative classification results are significantly improved under the same multi-path environment.