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Channel tracking in IRS-based UAV communication systems using federated learning

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H. Yang, J. Zhao, Z. Xiong, Y. Lam, S. Sun and L. Xiao, “Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 10, pp. 3144-3159, Oct. 2021, doi: 10.1109/JSAC.2021.3088655 Search in Google Scholar

Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. S. Bahai, “Energy Efficient Federated Learning Over Wireless Communication Networks," IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 1935-1949, March 2021, doi: 10.1109/TWC.2020.3037554 Search in Google Scholar

S. Al-Emadi and F. Al-Senaid, “Drone Detection Approach Based on Radio-Frequency Using Convolutional Neural Network,” IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pp. 29-34, 2020, doi: 10.1109/ICIoT48696.2020.9089489 Search in Google Scholar

B. Khamidehi and E. S. Sousa, “Federated Learning for Cellular-Connected UAVs: Radio Mapping and Path Planning,” GLOBECOM 2020 - 2020 IEEE Global Communications Conference, pp. 1-6, 2020, doi: 10.1109/GLOBECOM42002.2020.9322349 Search in Google Scholar

A. Bouguettaya, H. Zarzour, and A. Kechida, “Deep learning techniques to classify agricultural crops through UAV imagery: a review,” Neural Computer & Application vol 34, pp.9511–9536, 2022, https://doi.org/10.1007/s00521-022-07104-9 Search in Google Scholar

X. Song, Y. Zhao, Z. Wu, Z. Yang and J. Tang, “Joint Trajectory and Communication Design for IRS-Assisted UAV Networks,” IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1538-1542, July 2022, doi: 10.1109/LWC.2022.3179028 Search in Google Scholar

W. Wang, Y. Li, Q. Xu, B. Peng, M. Lu and Q. Lu, “Design of IRS-assisted UAV System for Transmission Line Inspection,” IEEE 21st International Conference on Communication Technology (ICCT), Tianjin, China, pp. 675-680, 2021, doi:10.1109ICCT52962.2021.9657890 Search in Google Scholar

S. K. Singh, K. Agrawal, K. Singh, C.P. Li and Z. Ding, “NOMA Enhanced Hybrid RIS-UAV-Assisted Full-Duplex Communication System with Imperfect SIC and CSI,” IEEE Transactions on Communications, vol. 70, no. 11, pp. 7609-7627, Nov. 2022, doi: 10.1109/TCOMM.2022.3212729 Search in Google Scholar

J. Feng, B. Zang, C. You, F. Chen, S. Chao, W. Che and Q. Xue, “Joint Passive Beamforming and Deployment Design for Dual Distributed-IRS Aided Communication," IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 13758-13763, Oct. 2023, doi: 10.1109/TVT.2023.3278699. Search in Google Scholar

X. Wang, F. Shu, Y. Wu, S. Yan, Y. Zhao, Q. Cheng and J. Wang, “Beamforming Design for IRS-and-UAV-aided Two-way Amplify-and-Forward Relay Networks,” IEEE Transactions on Communications, vol. 18, no. 11, pp. 1-6, 2022, https://doi.org/10.48550/arXiv.2306.00412 Search in Google Scholar

C. Wang, X. Chen, J. An, Z. Xiong, C. Xing, N. Zhao and D. Niyato, “Covert Communication Assisted by UAV-IRS,” IEEE Transactions on Communications, vol. 71, no. 1, pp. 357-369, Jan. 2023, doi: 10.1109/TCOMM.2022.3220903. Search in Google Scholar

J. Zhou, S. Zhang and Q Lu, “A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things,” IEEE Access, vol. 8, pp.140699-140725, 2020, https://doi.org/10.48550/arXiv.2104.10501 Search in Google Scholar

S. Tang, W. Zhou, L. Chen, L. Lai, J. Xia, and L. Fan, “Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks,” Elsevier, vol 47, 101381, August 2021, doi: https://doi.org/10.1016/j.phycom.2021.101381 Search in Google Scholar

A. I. Khan, and Y. Al-Mulla, “Unmanned Aerial Vehicle in the Machine Learning Environment,” Procedia Computer Science, vol.160, pp.46-53, ISSN0509, https://doi.org/10.1 Search in Google Scholar

I. Sharma, A. Sharma and S.K Gupta, “Asynchronous and Synchronous Federated Learning-based UAVs,” IEEE 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP 2023), Faculty of Engineering, Kasetsart University, Bangkok, Thailand, pp.18-20, January 2023, 10.1109/ICASYMP56348.2023.10044951 Search in Google Scholar

Y. S. Mandloi, and Y. Inada, “Machine Learning Approach for Drone Perception and Control”, Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-636 Search in Google Scholar

B. Taha and A. Shoufan, “Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research,” IEEE Access, vol. 7, pp. 138669-138682, 2019 doi:10.1109/ACCESS.2019.2942944. Search in Google Scholar

I. Sharma, S.K Gupta, A. Mishra and S. Askar, “Synchronous Federated Learning based Multi Unmanned Aerial Vehicles for Secure Applications,” Scalable Computing: Practice and Experience, vol. 24(3), pp. 191-201, 2023, DOI 10.12694/scpe.v24i3.2136 Search in Google Scholar

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