The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
A Survey on Machine-Learning Techniques for UAV-Based Communications
Open accessAbstract
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.
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Cites
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Cited by
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey
Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Mendoza Montoya, J. F., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 668
UAV remote sensing applications in marine monitoring: Knowledge visualization and review
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Heidari, A., Jafari Navimipour, N., Unal, M., & Zhang, G. (2023) · ACM Computing Surveys · doi:10.1145/3571728 · Cited by 195
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68
“…AI with respect to space applications have either been reviews concerning closely related aspects of space missions (([5], [7]) or detailed topical reviews with respect to the development and application of specific AI techniques or algorithms ([15], [11] and [16]) to a specific field ([17]).”
Background
Metadata
- Authors
- Bithas, P. S., Michailidis, E. T., Nomikos, N., Vouyioukas, D., & Kanatas, A. G.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-11-26
- Volume
- 19
- Issue
- 23
- Pages
- 5170
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2990204812
- Cited by
- 353
- References
- 100
- FWCI
- 25.99
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bithas, P. S., Michailidis, E. T., Nomikos, N., Vouyioukas, D., & Kanatas, A. G. (2019). A Survey on Machine-Learning Techniques for UAV-Based Communications. Sensors. https://doi.org/10.3390/s19235170
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