A Survey on Machine-Learning Techniques for UAV-Based Communications

Petros S Bithas; Emmanouel T Michailidis; Nikolaos Nomikos; Demosthenes Vouyioukas; Athanasios G Kanatas

doi:10.3390/s19235170

A Survey on Machine-Learning Techniques for UAV-Based Communications

Sensors (Basel). 2019 Nov 26;19(23):5170. doi: 10.3390/s19235170.

Authors

Petros S Bithas¹, Emmanouel T Michailidis², Nikolaos Nomikos³, Demosthenes Vouyioukas³, Athanasios G Kanatas⁴

Affiliations

¹ General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece.
² Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attica, Ancient Olive Grove Campus, 12244 Aigaleo, Greece.
³ Department of Information and Communication Systems Engineering, School of Engineering, University of the Aegean, 83200 Samos, Greece.
⁴ Department of Digital Systems, School of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, Greece.

Abstract

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

Keywords: 5G networks; air-to-ground communications; cellular networks; machine-learning; unmanned aerial vehicles (UAVs).

Publication types

Review