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

被引:171
|
作者
Bithas, Petros S. [1 ]
Michailidis, Emmanouel T. [2 ]
Nomikos, Nikolaos [3 ]
Vouyioukas, Demosthenes [3 ]
Kanatas, Athanasios G. [4 ]
机构
[1] Natl & Kapodistrian Univ Athens, Gen Dept, Psahna 34400, Evia, Greece
[2] Univ West Attica, Fac Engn, Dept Elect & Elect Engn, Ancient Olive Grove Campus, Aigaleo 12244, Greece
[3] Univ Aegean, Sch Engn, Dept Informat & Commun Syst Engn, Samos 83200, Greece
[4] Univ Piraeus, Sch Informat & Commun Technol, Dept Digital Syst, Piraeus 18534, Greece
关键词
5G networks; air-to-ground communications; machine-learning; unmanned aerial vehicles (UAVs); cellular networks; WIRELESS NETWORKS; NEURAL-NETWORKS; TRAJECTORY DESIGN; PHYSICAL LAYER; PATH LOSS; PREDICTION; OPTIMIZATION; DEPLOYMENT; SECURITY; PLACEMENT;
D O I
10.3390/s19235170
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:39
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