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An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweigh...

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Detalles Bibliográficos
Autores principales: Roy, Chirag, Yadav, Satyendra Singh, Pal, Vipin, Singh, Mangal, Patra, Sarat Kumar, Sinha, G. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8691989/
https://www.ncbi.nlm.nih.gov/pubmed/34950200
http://dx.doi.org/10.1155/2021/5047355
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author Roy, Chirag
Yadav, Satyendra Singh
Pal, Vipin
Singh, Mangal
Patra, Sarat Kumar
Sinha, G. R.
author_facet Roy, Chirag
Yadav, Satyendra Singh
Pal, Vipin
Singh, Mangal
Patra, Sarat Kumar
Sinha, G. R.
author_sort Roy, Chirag
collection PubMed
description With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from −20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones.
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spelling pubmed-86919892021-12-22 An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks Roy, Chirag Yadav, Satyendra Singh Pal, Vipin Singh, Mangal Patra, Sarat Kumar Sinha, G. R. Comput Intell Neurosci Research Article With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from −20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones. Hindawi 2021-12-14 /pmc/articles/PMC8691989/ /pubmed/34950200 http://dx.doi.org/10.1155/2021/5047355 Text en Copyright © 2021 Chirag Roy et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Roy, Chirag
Yadav, Satyendra Singh
Pal, Vipin
Singh, Mangal
Patra, Sarat Kumar
Sinha, G. R.
An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks
title An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks
title_full An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks
title_fullStr An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks
title_full_unstemmed An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks
title_short An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks
title_sort ensemble deep learning model for automatic modulation classification in 5g and beyond iot networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8691989/
https://www.ncbi.nlm.nih.gov/pubmed/34950200
http://dx.doi.org/10.1155/2021/5047355
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