Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey
The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9399081/ |
id |
doaj-fe2aa51fd39d4b178b46bfb1ba45202f |
---|---|
record_format |
Article |
spelling |
doaj-fe2aa51fd39d4b178b46bfb1ba45202f2021-04-19T23:00:43ZengIEEEIEEE Access2169-35362021-01-019578515787310.1109/ACCESS.2021.30718019399081Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive SurveyBachir Jdid0https://orcid.org/0000-0003-1805-8545Kais Hassan1https://orcid.org/0000-0001-7455-5242Iyad Dayoub2https://orcid.org/0000-0003-0910-4722Wei Hong Lim3Mastaneh Mokayef4Faculty of Engineering Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaLaboratoire d’Acoustique de l’Université du Mans (LAUM) –UMR CNRS 6613, Le Mans University, Le Mans, FranceDépartement d’Opto-Acousto-Électronique (DOAE), Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), CNRS, Université Polytechnique Hauts-de-France, Université de Lille, ISEN, Centrale Lille, UMR 8520, Valenciennes, FranceFaculty of Engineering Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaFaculty of Engineering Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaThe rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.https://ieeexplore.ieee.org/document/9399081/Automatic modulation recognitiondeep learningmachine learningMIMOSISOwireless signal classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bachir Jdid Kais Hassan Iyad Dayoub Wei Hong Lim Mastaneh Mokayef |
spellingShingle |
Bachir Jdid Kais Hassan Iyad Dayoub Wei Hong Lim Mastaneh Mokayef Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey IEEE Access Automatic modulation recognition deep learning machine learning MIMO SISO wireless signal classification |
author_facet |
Bachir Jdid Kais Hassan Iyad Dayoub Wei Hong Lim Mastaneh Mokayef |
author_sort |
Bachir Jdid |
title |
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey |
title_short |
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey |
title_full |
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey |
title_fullStr |
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey |
title_full_unstemmed |
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey |
title_sort |
machine learning based automatic modulation recognition for wireless communications: a comprehensive survey |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion. |
topic |
Automatic modulation recognition deep learning machine learning MIMO SISO wireless signal classification |
url |
https://ieeexplore.ieee.org/document/9399081/ |
work_keys_str_mv |
AT bachirjdid machinelearningbasedautomaticmodulationrecognitionforwirelesscommunicationsacomprehensivesurvey AT kaishassan machinelearningbasedautomaticmodulationrecognitionforwirelesscommunicationsacomprehensivesurvey AT iyaddayoub machinelearningbasedautomaticmodulationrecognitionforwirelesscommunicationsacomprehensivesurvey AT weihonglim machinelearningbasedautomaticmodulationrecognitionforwirelesscommunicationsacomprehensivesurvey AT mastanehmokayef machinelearningbasedautomaticmodulationrecognitionforwirelesscommunicationsacomprehensivesurvey |
_version_ |
1721519131396669440 |