Classification of Spectrally Efficient Constant Envelope Modulations Based on Radial Basis Function Network and Deep Learning

Despite its significance, modulation classification of constant envelope modulations (CEM) has not gained worthy attention in AMC literature so far. Two neural network-based architectures, i.e., radial basis function network (RBFN) and sparse-autoencoder-based deep neural network (DNN) are proposed...

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Bibliographic Details
Published in:IEEE communications letters 2019-09, Vol.23 (9), p.1529-1533
Main Authors: Shah, Maqsood Hussain, Dang, Xiaoyu
Format: Article
Language:English
Subjects:
Artificial neural networks
Baseband
Classification
Deep learning
Doppler effect
Environmental impact
Fading channels
Frequency modulation
frequency selective
hybrid features
Neural networks
Normal distribution
Radial basis function
radial basis function (RBF)
Random noise
Selective fading
sparse-autoencoder (SAE)
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Summary:Despite its significance, modulation classification of constant envelope modulations (CEM) has not gained worthy attention in AMC literature so far. Two neural network-based architectures, i.e., radial basis function network (RBFN) and sparse-autoencoder-based deep neural network (DNN) are proposed and analyzed for the classification of spectrally efficient CEM modulations. A blind classification method which does not require any a-priori information about the channel or CEM specifics is based on the effectiveness of proposed hybrid feature space (HFS), used to train the trending neural network classifiers. Classification performance of both networks is analyzed for the typical additive white Gaussian noise (AWGN) channel and less explored, unfriendly, frequency-selective fading environment under the impact of Doppler shift.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2927348