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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Vydáno v:	IET communications Ročník 13; číslo 4; s. 423 - 432
Hlavní autoři:	Shah, Maqsood Hussain, Dang, Xiaoyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 05.03.2019
Témata:	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
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Popis
Shrnutí:	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 authors propose a set of low complexity spectral and cumulant based features for training SAE-DNN. The network is designed using forced dimensionality reduction and sparsity constraint to achieve a low complexity solution with improved ability to learn more refined and robust features from the input training data. A unique training method is presented in this study which incorporates a range of SNR values for the entire span of the training dataset, as compared to the conventional approach which only uses a single SNR value for all the training examples. A comprehensive performance analysis shows that the proposed method outperforms many conventional counterparts in the literature. Generalisation test verifies that network is feasible for all channel conditions. A robust classification behaviour is observed against phase-frequency impairments and Doppler shift for frequency selective fading scenarios.
ISSN:	1751-8628 1751-8636
DOI:	10.1049/iet-com.2018.5688