Semi-supervised Learning for Automatic Modulation Recognition Using Haar Time–Frequency Mask and Positional–Spatial Attention

Abstract

Automatic modulation recognition plays an important role in many military and civilian applications, including cognitive radio, spectrum sensing, signal surveillance, and interference identification. Due to the powerful ability of deep learning to extract hidden features and perform classification, it can extract highly separative features from massive signal samples. Considering the condition of limited training samples, we propose a semi-supervised learning framework based on Haar time–frequency (HTF) mask data augmentation and the positional–spatial attention (PSA) mechanism. Specifically, the HTF mask is designed to increase data diversity, and the PSA is designed to address the limited receptive field of the convolutional layer and enhance the feature extraction capability of the constructed network. Extensive experimental results obtained on the public RML2016.10a dataset show that the proposed semi-supervised framework utilizes 1% of the given labeled data and reaches a recognition accuracy of 92.09% under 6 dB signals.