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A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks
Abstract
Recently, the UAS has been extensively exploited for data collection from remote and dangerous or inaccessible areas. While most of its existing applications have been directed toward surveillance and monitoring tasks, the UAS can play a significant role as a communication network facilitator. For example, the UAS may effectively extend communication capability to disaster-affected people (who have lost cellular and Internet communication infrastructures on the ground) by quickly constructing a communication relay system among a number of UAVs. However, the distance between the centers of trajectories of two neighboring UAVs, referred to as IUD, plays an important role in the communication delay and throughput. For instance, the communication delay increases rapidly while the throughput is degraded when the IUD increases. In order to address this issue, in this article, we propose a simple but effective dynamic trajectory control algorithm for UAVs. Our proposed algorithm considers that UAVs with queue occupancy above a threshold are experiencing congestion resulting in communication delay. To alleviate the congestion at UAVs, our proposal adjusts their center coordinates and also, if needed, the radius of their trajectory. The performance of our proposal is evaluated through computer-based simulations. In addition, we conduct several field experiments in order to verify the effectiveness of UAV-aided networks.
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Citations
Cited by
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018) · Networks · doi:10.1002/net.21818 · Cited by 985
“Serving as flying base stations, several loitering drones may form a multihop communication network, in which a mobile device connected with one drone may transmit data to a mobile device connected to some other drone in the network [77, 98, 99, 248].”
BackgroundEvolution 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
Distributed Multi-Agent Meta Learning for Trajectory Design in Wireless Drone Networks
Hu, Y., Chen, M., Saad, W., Poor, H. V., & Cui, S. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3088689 · Cited by 152
Metadata
- Authors
- Fadlullah, Z. M., Takaishi, D., Nishiyama, H., Kato, N., & Miura, R.
- Venue
- IEEE Network
- Published
- 2016-01
- Volume
- 30
- Issue
- 1
- Pages
- 100-105
- ISSN
- 0890-8044 (ISSN-L)
- OpenAlex
- W2285553406
- Cited by
- 212
- References
- 0
- FWCI
- 37.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks. https://app.cochat.ai/papers/10.1109/mnet.2016.7389838
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- Fadlullah, Z. M., Takaishi, D., Nishiyama, H., Kato, N., & Miura, R. (2016). A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks. IEEE Network. https://doi.org/10.1109/mnet.2016.7389838
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