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• W4285266496 abstract "Recent years have witnessed a surge of developments in deep learning (DL) motivated by a variety of contemporary applications. The conventional DL-based automatic modulation classification (AMC) methods are always relying on a great quan- tity of data. In this paper, we propose a DL-based AMC model with short data for spectrum sensing of wireless communication signals. First, a hopping transform unit is proposed to represent the transient variation occurred either by frequency, amplitude or phase modulations. Second, a bidirectional long short-term memory (Bi-LSTM) based hopping feature perception model, namely deep-learning hopping capture model (DHCM), is built for the AMC. A comprehensive comparison of the DHCM with other existing methods is then provided under various signal-to- noise ratios (SNRs). The experimental results demonstrate the superiority of the proposed method under short data." @default.
• W4285266496 created "2022-07-14" @default.
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• W4285266496 date "2022-01-01" @default.
• W4285266496 modified "2023-10-17" @default.
• W4285266496 title "Deep-learning Hopping Capture Model for Automatic Modulation Classification of Wireless Communication Signals" @default.
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• W4285266496 doi "https://doi.org/10.1109/taes.2022.3189335" @default.
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