Cyclostationary Feature Based Modulation Classification With Convolutional Neural Network in Multipath Fading Channels

Abstract

Modulation classification has been widely studied in recent years. However, few studies focus on the performance degradation in multipath fading channels, whose impact is non-negligible. In this paper, a convolutional neural network (CNN) employing cyclostationary (CS) feature, which maintain the essential characteristics in fading channels, is proposed for robust modulation classification. Our method can be implemented in two approaches, referred as CASE1 and CASE2. In CASE1, a single-structured CNN is designed for learning hybrid CS features to perform classification. And in CASE2, we present a CNN model based on a hierarchical structure to perform two-stage classification. Specifically, the coarse classification is performed by learning the second-order CS features with the first-level CNN. Next, another CNN can be selectively activated to learn from high-order CS features for fine classification within the subclass. In this way, our method uses CS features to provide favorable guidance for the learning process of CNN, thus improving the classification performance in fading channels. The experimental results demonstrate the advantages of the proposed method in terms of classification accuracy and computational complexity.