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

Sun, Chenrui; Fontanesi, Gianluca; Canberk, Berk; Mohajerzadeh, Amirhossein; Chatzinotas, Symeon; Grace, David; Ahmadi, Hamed

Abstract

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. INDEX TERMS Unmanned aerial vehicle, 6G, federated learning, transfer learning, meta learning, and explainable AI.

Citation

Sun, C., Fontanesi, G., Canberk, B., Mohajerzadeh, A., Chatzinotas, S., Grace, D., & Ahmadi, H. (2024). Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques. IEEE Open Journal of Vehicular Technology, 5, 825-854. https://doi.org/10.1109/ojvt.2024.3401024

• Journal Article Type: Article
• Acceptance Date: May 9, 2024
• Online Publication Date: May 15, 2024
• Publication Date: 2024
• Deposit Date: Oct 10, 2024
• Publicly Available Date: Oct 11, 2024
• Journal: IEEE Open Journal of Vehicular Technology
• Print ISSN: 2644-1330
• Electronic ISSN: 2644-1330
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Pages: 825-854
• DOI: https://doi.org/10.1109/ojvt.2024.3401024
• Keywords: Unmanned aerial vehicle , 6G, federated learning, transfer learning, meta learning, explainable AI