https://scholars.lib.ntu.edu.tw/handle/123456789/611226
標題: | Polar feature based deep architectures for automatic modulation classification considering channel fading | 作者: | AN-YEU(ANDY) WU | 關鍵字: | Automatic modulation classification; Channel fading; Convolutional neural network; Deep learning | 公開日期: | 2019 | 起(迄)頁: | 554-558 | 來源出版物: | 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings | 摘要: | To develop intelligent receivers, automatic modulation classification (AMC) plays an important role for better spectrum utilization. The emerging deep learning (DL) technique has received much attention in AMC due to its superior performance in classifying data with deep structure. In this work, a novel polar-based deep learning architecture with channel compensation network (CCN) is proposed. Our test results show that learning features from polar domain (r-θ) can improve recognition accuracy by 5% and reduce training overhead by 48%. Besides, the proposed CCN is also robust to channel fading, such as amplitude and phase offsets, and can improve the recognition accuracy by 14% under practical channel environments. © 2018 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063105038&doi=10.1109%2fGlobalSIP.2018.8646375&partnerID=40&md5=d23b708ad8e9fd8bb6434ea3c84e889a https://scholars.lib.ntu.edu.tw/handle/123456789/611226 |
ISBN: | 9.78173E+12 | DOI: | 10.1109/GlobalSIP.2018.8646375 | SDG/關鍵字: | Fading channels; Modulation; Network architecture; Neural networks; Automatic modulation classification; Automatic modulation classification (AMC); Channel compensation; Convolutional neural network; Deep architectures; Learning architectures; Recognition accuracy; Spectrum utilization; Deep learning |
顯示於: | 電機工程學系 |
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