Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks

Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional ove...

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Autores principales:	Zeeshan Kaleem, Muhammad Ali, Ishtiaq Ahmad, Waqas Khalid, Ahmed Alkhayyat, Abbas Jamalipour
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Automatic modulation classification artificial intelligence deep learning real-time signal detection USRP Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/617d8ea16aaf4b29b305399a4fd1ac5a
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spelling	oai:doaj.org-article:617d8ea16aaf4b29b305399a4fd1ac5a2021-11-26T00:00:38ZArtificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks2169-353610.1109/ACCESS.2021.3128508https://doaj.org/article/617d8ea16aaf4b29b305399a4fd1ac5a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615189/https://doaj.org/toc/2169-3536Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional overhead in the signal. In this paper, we developed a universal software radio peripheral (USRP) based intelligent AMC system to detect and classify various digital modulation schemes in real-time. For each modulation scheme, we extracted different spectral features for different values of signal-to-noise ratio (SNR) values. Based on the extracted features, we train the neural network to classify the modulation schemes. Experimental results show that we achieve around 97% classification accuracy in real-time as compared to the existing offline classification schemes. Moreover, we also compare the performance of the proposed model with HisarMod2019.1 model in terms of various metrics such as cross-entropy and mean square error. Results clearly demonstrates the efficiency of the proposal for real-time implementation and classification.Zeeshan KaleemMuhammad AliIshtiaq AhmadWaqas KhalidAhmed AlkhayyatAbbas JamalipourIEEEarticleAutomatic modulation classificationartificial intelligencedeep learningreal-time signal detectionUSRPElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155584-155597 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Automatic modulation classification artificial intelligence deep learning real-time signal detection USRP Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Automatic modulation classification artificial intelligence deep learning real-time signal detection USRP Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zeeshan Kaleem Muhammad Ali Ishtiaq Ahmad Waqas Khalid Ahmed Alkhayyat Abbas Jamalipour Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
description	Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional overhead in the signal. In this paper, we developed a universal software radio peripheral (USRP) based intelligent AMC system to detect and classify various digital modulation schemes in real-time. For each modulation scheme, we extracted different spectral features for different values of signal-to-noise ratio (SNR) values. Based on the extracted features, we train the neural network to classify the modulation schemes. Experimental results show that we achieve around 97% classification accuracy in real-time as compared to the existing offline classification schemes. Moreover, we also compare the performance of the proposed model with HisarMod2019.1 model in terms of various metrics such as cross-entropy and mean square error. Results clearly demonstrates the efficiency of the proposal for real-time implementation and classification.
format	article
author	Zeeshan Kaleem Muhammad Ali Ishtiaq Ahmad Waqas Khalid Ahmed Alkhayyat Abbas Jamalipour
author_facet	Zeeshan Kaleem Muhammad Ali Ishtiaq Ahmad Waqas Khalid Ahmed Alkhayyat Abbas Jamalipour
author_sort	Zeeshan Kaleem
title	Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
title_short	Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
title_full	Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
title_fullStr	Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
title_full_unstemmed	Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
title_sort	artificial intelligence-driven real-time automatic modulation classification scheme for next-generation cellular networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/617d8ea16aaf4b29b305399a4fd1ac5a
work_keys_str_mv	AT zeeshankaleem artificialintelligencedrivenrealtimeautomaticmodulationclassificationschemefornextgenerationcellularnetworks AT muhammadali artificialintelligencedrivenrealtimeautomaticmodulationclassificationschemefornextgenerationcellularnetworks AT ishtiaqahmad artificialintelligencedrivenrealtimeautomaticmodulationclassificationschemefornextgenerationcellularnetworks AT waqaskhalid artificialintelligencedrivenrealtimeautomaticmodulationclassificationschemefornextgenerationcellularnetworks AT ahmedalkhayyat artificialintelligencedrivenrealtimeautomaticmodulationclassificationschemefornextgenerationcellularnetworks AT abbasjamalipour artificialintelligencedrivenrealtimeautomaticmodulationclassificationschemefornextgenerationcellularnetworks
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Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks

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