Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition

Abstract

Recently, deep learning-based image classification and speech recognition approaches have made extensive use of attention mechanisms to achieve state-of-the-art recognition, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals are crucial for modulation recognition, this letter proposes a time-frequency attention mechanism for convolutional neural network (CNN)-based automatic modulation recognition. The proposed time-frequency attention mechanism is designed to learn which channel, frequency and time information is more meaningful in CNN for modulation recognition. We analyze the effectiveness of the proposed attention mechanism and evaluate the performance of the proposed models. Experiment results show that the proposed methods outperform existing learning-based methods and attention mechanisms.