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Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication
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DOI：10.1016/j.patcog.2023.109345.png)
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摘要
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Deep neural networks have been recently applied in automatic modulation classification task and achieved remarkable success. However, Existing neural networks mainly focus on the purely data-driven architecture design, and fail to explore the hand-crafted feature mechanisms which are particularly sig-nificant for radio signal presentation in wireless communication. Inspired by digital signal processing theories, we propose frequency learning attention networks (FLANs) to analyze the radio spectral bias from frequency perspective, based on a multi-spectral attention mechanism for learning-based frequency components selection. FLANs are the general case of classical global average pooling and leverage iden-tical structures of the popular neural networks. Extensive experiments have been conducted to validate the superiority of FLANs for automatic modulation classification over a wide variety of state-of-the-art methods on RADIOML 2018.01A dataset.(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Frequency learning
Attention mechanism
Automatic modulation classification
Wireless communication
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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