Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks

TL;DR

This paper introduces a novel dual-driven automatic modulation classification scheme that combines visual and attribute learning models to improve accuracy, especially in low SNR conditions, outperforming traditional methods.

Contribution

The paper proposes a new radio frequency machine learning approach that integrates visual features and attribute semantic representations for enhanced modulation recognition.

Findings

Achieves higher classification accuracy than benchmark schemes.

Reduces confusion among high-order modulations.

Performs well in low SNR environments.

Abstract

Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.