| Literature DB >> 31779133 |
Petros S Bithas1, Emmanouel T Michailidis2, Nikolaos Nomikos3, Demosthenes Vouyioukas3, Athanasios G Kanatas4.
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.Entities:
Keywords: 5G networks; air-to-ground communications; cellular networks; machine-learning; unmanned aerial vehicles (UAVs)
Year: 2019 PMID: 31779133 PMCID: PMC6929112 DOI: 10.3390/s19235170
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576