TSN-A: An Efficient Deep Learning Model for Automatic Modulation Classification Based on Intra-Class Confusion Reduction of Modulation Families

Abstract

Automatic modulation classification (AMC) is an impressive technology, which is widely used in military and civilian fields. Recently, deep learning-based AMC (DL-AMC) methods have shown outstanding performance. However, most DL-AMC methods employ a single-stage network framework, which is effective for reducing only inter-class confusion of modulation families, while the intra-class confusion of modulation families attracts less attention and remains to be addressed. This letter proposes a two-stage network with attention (TSN-A), which consists of a main network and two sub-networks. In the first stage, the main network can effectively tackle the problem of inter-class confusion. In the second stage, the sub-networks can efficiently reduce the intra-class confusion of AM-SSB and AM-DSB families. TSN-A achieves the top results on the public challenging RML 2018.01a dataset and is more competitive in low SNR environments. Meanwhile, our main network exhibits stronger and more robust classification ability on different datasets than the benchmark models. Moreover, our sub-networks are very applicable to be migrated to other DL-AMC models for helping them improve the classification accuracy by reducing the intra-class confusion.