Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse-autoencoder-based deep neural network

Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse-autoencoder-based deep neural network (SAE-DNN) is proposed to deal with the problem of AMC for much neglected frequency selective fading scenarios with Doppler shift. The...

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Veröffentlicht in:	IET communications Jg. 13; H. 4; S. 423 - 432
Hauptverfasser:	Shah, Maqsood Hussain, Dang, Xiaoyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 05.03.2019
Schlagworte:	AMC cumulant based features deep learning Doppler shift feature extraction higher order statistics input training data learning (artificial intelligence) low complexity solution low complexity spectral modulation neglected frequency selective fading scenarios neural nets refined features Research Article robust approach robust features signal classification training SAE‐DNN unique training method unsupervised sparse‐autoencoder‐based deep neural network input training data modulation unique training method neglected frequency selective fading scenarios higher order statistics training SAE-DNN signal classification Doppler shift deep learning low complexity spectral feature extraction AMC robust approach refined features cumulant based features robust features unsupervised sparse-autoencoder-based deep neural network learning (artificial intelligence) low complexity solution neural nets
ISSN:	1751-8628, 1751-8636
Online-Zugang:	Volltext
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