Rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57523-5_21
Reference19 articles.
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2. Alzenad, M., El-Keyi, A., Yanikomeroglu, H.: 3-D placement of an unmanned aerial vehicle base station for maximum coverage of users with different QoS requirements. IEEE Wirel. Commun. Lett. 7(1), 38–41 (2018). https://doi.org/10.1109/LWC.2017.2752161
3. Anokye, S., Ayepah-Mensah, D., Seid, A.M., Boateng, G.O., Sun, G.: Deep reinforcement learning-based mobility-aware UAV content caching and placement in mobile edge networks. IEEE Syst. J. 16(1), 275–286 (2022). https://doi.org/10.1109/JSYST.2021.3082837
4. Arif, T.Y., Munadi, R., et al.: Evaluation of the minstrel-HT rate adaptation algorithm in IEEE 802.11 n WLANs. Int. J. Simul. Syst. Sci. Technol. 18(1) (2017)
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