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Open AccessArticle

Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network

Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea
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Entropy 2017, 19(9), 454; https://doi.org/10.3390/e19090454
Received: 31 July 2017 / Revised: 25 August 2017 / Accepted: 28 August 2017 / Published: 30 August 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
In this paper, we propose a deep neural network (DNN)-based automatic modulation classification (AMC) for digital communications. While conventional AMC techniques perform well for additive white Gaussian noise (AWGN) channels, classification accuracy degrades for fading channels where the amplitude and phase of channel gain change in time. The key contributions of this paper are in two phases. First, we analyze the effectiveness of a variety of statistical features for AMC task in fading channels. We reveal that the features that are shown to be effective for fading channels are different from those known to be good for AWGN channels. Second, we introduce a new enhanced AMC technique based on DNN method. We use the extensive and diverse set of statistical features found in our study for the DNN-based classifier. The fully connected feedforward network with four hidden layers are trained to classify the modulation class for several fading scenarios. Numerical evaluation shows that the proposed technique offers significant performance gain over the existing AMC methods in fading channels. View Full-Text
Keywords: automatic modulation classification; deep neural network; fading channel; feature selection; feature extraction automatic modulation classification; deep neural network; fading channel; feature selection; feature extraction
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Lee, J.H.; Kim, J.; Kim, B.; Yoon, D.; Choi, J.W. Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network. Entropy 2017, 19, 454.

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