Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques
This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing rese...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10531095/ |
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| author | Chenrui Sun Gianluca Fontanesi Berk Canberk Amirhossein Mohajerzadeh Symeon Chatzinotas David Grace Hamed Ahmadi |
| author_facet | Chenrui Sun Gianluca Fontanesi Berk Canberk Amirhossein Mohajerzadeh Symeon Chatzinotas David Grace Hamed Ahmadi |
| author_sort | Chenrui Sun |
| collection | DOAJ |
| description | This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of state-of-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted. |
| format | Article |
| id | doaj-art-35ffc6850f144752a0c28c02b5244000 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-35ffc6850f144752a0c28c02b52440002025-01-30T00:04:09ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-01582585410.1109/OJVT.2024.340102410531095Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning TechniquesChenrui Sun0https://orcid.org/0000-0002-6140-5285Gianluca Fontanesi1https://orcid.org/0000-0002-8198-7497Berk Canberk2https://orcid.org/0000-0001-6472-1737Amirhossein Mohajerzadeh3https://orcid.org/0000-0002-2630-0429Symeon Chatzinotas4https://orcid.org/0000-0001-5122-0001David Grace5https://orcid.org/0000-0003-4493-7498Hamed Ahmadi6https://orcid.org/0000-0001-5508-8757School of Physics, Engineering and Technology, University of York, York, U.K.Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg-Kirchberg, LuxembourgSchool of Computing, Engineering and The Built Environment, Edinbrough Napier University, Edinburgh, U.K.Department of Computing and Information Technology, Sohar University, Sohar, OmanInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg-Kirchberg, LuxembourgSchool of Physics, Engineering and Technology, University of York, York, U.K.School of Physics, Engineering and Technology, University of York, York, U.K.This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of state-of-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.https://ieeexplore.ieee.org/document/10531095/Unmanned aerial vehicle6Gfederated learningtransfer learningmeta learningand explainable AI |
| spellingShingle | Chenrui Sun Gianluca Fontanesi Berk Canberk Amirhossein Mohajerzadeh Symeon Chatzinotas David Grace Hamed Ahmadi Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques IEEE Open Journal of Vehicular Technology Unmanned aerial vehicle 6G federated learning transfer learning meta learning and explainable AI |
| title | Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques |
| title_full | Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques |
| title_fullStr | Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques |
| title_full_unstemmed | Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques |
| title_short | Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques |
| title_sort | advancing uav communications a comprehensive survey of cutting edge machine learning techniques |
| topic | Unmanned aerial vehicle 6G federated learning transfer learning meta learning and explainable AI |
| url | https://ieeexplore.ieee.org/document/10531095/ |
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