Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19235170 | SENSORS |
Keywords | DocType | Volume |
5G networks,air-to-ground communications,machine-learning,unmanned aerial vehicles (UAVs),cellular networks | Journal | 19 |
Issue | ISSN | Citations |
23.0 | 1424-8220 | 8 |
PageRank | References | Authors |
0.45 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petros S. Bithas | 1 | 153 | 21.69 |
| Emmanouel T. Michailidis | 2 | 8 | 0.45 |
| Nikolaos Nomikos | 3 | 198 | 16.95 |
| Demosthenes D. Vouyioukas | 4 | 251 | 27.71 |
| Athanasios G. Kanatas | 5 | 193 | 30.18 |