Learning of Frequency-Time Attention Mechanism for Automatic Modulation Recognition
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Recent learning-based image classification and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, 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 mode recognition, this paper proposes a frequency-time attention mechanism for a convolutional neural network (CNN)-based modulation recognition framework. The proposed frequency-time attention module 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 frequency-time attention mechanism and compare the proposed method with two existing learning-based methods. Experiments on an open-source modulation recognition dataset show that the recognition performance of the proposed framework is better than those of the framework without frequency-time attention and existing learning-based methods.
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