Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

6G; and explainable AI; federated learning; meta learning; transfer learning; Unmanned aerial vehicle; 6 G; 6g mobile communication; Aerial vehicle; And explainable AI; Federated learning; Metalearning; Mobile communications; Task analysis; Transfer learning; Automotive Engineering; Autonomous aerial vehicles; Surveys; Artificial intelligence; Cables; Aircraft

Abstract :

[en] 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.

Disciplines :

Computer science

Author, co-author :

Sun, Chenrui ; University of York, School of Physics, Engineering and Technology, York, United Kingdom

FONTANESI, Gianluca ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > SigCom > Team Symeon CHATZINOTAS

Canberk, Berk ; Edinbrough Napier University, School of Computing, Engineering and the Built Environment, Edinburgh, United Kingdom ; Istanbul Technical University, Department of Artificial Intelligence and Data Engineering, Istanbul, Turkey

Mohajerzadeh, Amirhossein ; Sohar University, Department of Computing and Information Technology, Sohar, Oman

CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

Grace, David ; University of York, School of Physics, Engineering and Technology, York, United Kingdom

Ahmadi, Hamed ; University of York, School of Physics, Engineering and Technology, York, United Kingdom

External co-authors :

yes

Language :

English

Title :

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Publication date :

2024

Journal title :

IEEE Open Journal of Vehicular Technology

eISSN :

2644-1330

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

825 - 854

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx7/8782711/10345397/10531095.pdf?arnumber=10531095

Funders :

Engineering and Physical Sciences Research Council
Impact Acceleration Accounts

Available on ORBilu :

since 26 March 2026

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