Performance Analysis of Cooperative Automatic Modulation Classification using Higher Order Statistics

Abstract

Over the past few decades, many automatic modulation classification (AMC) algorithms were discussed based on their ability to classify the modulation scheme. However, in practical scenarios, the AMC algorithm should have low complexity, high speed, and better resource utilization. The majority of the current modulation classification methods have inferior performance in low SNRs. In this paper, a cooperative AMC (CAMC) approach using soft decision fusion based on the combination of fourth and sixth order cumulants over additive white Gaussian noise channel and multipath fading channel is proposed. This proposed approach improves the classification accuracy of the modulation schemes at low SNR comparatively.