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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1-31
Number of pages31
JournalIEEE Open Journal of Vehicular Technology
DOIs
Publication statusAccepted/In press - 2024
Externally publishedYes

Bibliographical note

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Keywords

  • 6G mobile communication
  • 6 G
  • Aircraft
  • and Explainable AI
  • Artificial intelligence
  • Autonomous aerial vehicles
  • Cables
  • Federated Learning
  • Meta Learning
  • Surveys
  • Task analysis
  • Transfer Learning
  • Unmanned Aerial Vehicle

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