|Title:||Polar feature based deep architectures for automatic modulation classification considering channel fading||Authors:||AN-YEU(ANDY) WU||Keywords:||Automatic modulation classification; Channel fading; Convolutional neural network; Deep learning||Issue Date:||2019||Start page/Pages:||554-558||Source:||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings||Abstract:||
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.
|ISBN:||9.78173E+12||DOI:||10.1109/GlobalSIP.2018.8646375||metadata.dc.subject.other:||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|
|Appears in Collections:||電機工程學系|
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