Modulation classification in multipath fading channels using sixth‐order cumulants and stacked convolutional auto‐encoders

Abstract

Automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. However, the performance of AMC is seriously degraded in multipath fading channels. Higher-order cumulants of the received signal are resistant to additive white Gaussian noise and multipath fading. Moreover, features extracted from unlabelled data using stacked convolutional auto-encoders (SCAEs) are comparable with or superior to most of the best hand-engineered ones. In this study, the authors propose a new algorithm, which applies sixth-order cumulants and SCAEs to modulation classification in multipath fading channels. Simulation results show that the proposed method can achieve better classification accuracy than the existing approaches under various channel conditions. The proposed approach is also robust to model imperfections such as phase, timing, and frequency offsets.