Aprendizado Federado e Deep Q-Network habilitando VANTs como Infraestrutura em Redes 6G
Abstract
The deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations is a key enabler for different emerging use cases of 6G networks. In this context, this article presents a proposition for the positioning of UAVs acting as mobile network infrastructure assisted by Federated Deep Learning (FDL) and Deep Q-Network (DQN). The proposal is based on a decentralized learning paradigm that improves the communication overhead with a focus on preserving privacy and dynamically adapting UAVs to the propagation environment of mobile networks.References
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Brasil 6G (2021b). Relatório Técnico das Atividades 5.1 e 5.2 Projeto e Seleção de Componentes, Plataformas, Ferramentas e Especificação. Disponível em: https://inatel.br/brasil6g. Acesso em: 07/03/2023.
Brik, B., Ksentini, A., and Bouaziz, M. (2020). Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems. IEEE Access, 8:53841–53849.
Hu, J., Zhang, H., Song, L., Han, Z., and Poor, H. V. (2020). Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management. IEEE Wireless Communications, 27(1):116–123.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B. (2021). A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering.
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., and Miao, C. (2020). Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Communications Surveys Tutorials, 22(3):2031–2063.
Liu, L., Zhao, Y., Qi, F., Zhou, F., Xie, W., He, H., and Zheng, H. (2022). Federated Deep Reinforcement Learning for Joint AeBSs Deployment and Computation Offloading in Aerial Edge Computing Network. Electronics, 11:3641.
Mahlool, D. H. and Abed, M. H. (2022). A Comprehensive Survey on Federated Learning: Concept and Applications. In Mobile Computing and Sustainable Informatics, pages 539–553, Singapore. Springer Nature Singapore.
P G, S. and Magarini, M. (2021). Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization. Electronics, 10:2953.
Shahbazi, A., Donevski, I., Nielsen, J. J., and Di Renzo, M. (2022). Federated Reinforcement Learning UAV Trajectory Design for Fast Localization of Ground Users. In 2022 30th European Signal Processing Conference (EUSIPCO), pages 663–666.
Published
2023-05-22
How to Cite
OLIVEIRA, Renan R. de; S. E SILVA, Rogério; FREITAS, Leandro A.; OLIVEIRA-JR, Antonio.
Aprendizado Federado e Deep Q-Network habilitando VANTs como Infraestrutura em Redes 6G. In: WORKSHOP DE REDES 6G (W6G), 3. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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p. 1-6.
DOI: https://doi.org/10.5753/w6g.2023.719.
