Anti-jamming transmission in softwarization UAV network: a federated deep reinforcement learning approach
Author:
Funder
Aeronautical Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11276-023-03500-8.pdf
Reference28 articles.
1. Wang, H., Zhao, H., Zhang, J., Ma, D., Li, J., & Wei, J. (2020). Survey on unmanned aerial vehicle networks: A cyber physical system perspective. IEEE Communications Surveys & Tutorials, 22, 1027–1070.
2. Mozaffari, M., Saad, W., Bennis, M., Nam, Y. H., & Debbah, M. (2019). A tutorial on uavs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys & Tutorials, 21, 2334–2360.
3. Zhang, Y., Qian, L., & Xu, H. (2021) Intelligent joint beamforming and distributed power control for UAV-assisted ultra-dense network: A hierarchical optimization approach. In IEEE national aerospace and electronics conference (pp. 184–190).
4. Zhu, L., Zhang, J., Xiao, Z., Cao, X., Xia, X. G., & Schober, R. (2020). Millimeter-wave full-duplex UAV relay: Joint positioning, beamforming, and power control. IEEE Journal on Selected Areas in Communications, 38, 2057–2073.
5. Rahmati, A., Hosseinalipour, S., Yapıcı, Y., Güvenç, İ., Dai, H., & Bhuyan, A. (2020). Energy-efficient beamforming and power control for uplink NOMA in mmWave UAV networks. In IEEE global communications conference (pp. 1–6).
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