Handover em redes aéreas 5G: Uma solução baseada em aprendizado por reforço
Resumo
Os Veículos Aéreos Não Tripulados (UAV) como estações rádio-base, servindo a usuários terrestres, vem ganhando tração com as novas releases 5G e futuros sistemas 6G. Fornecendo conectividade ubíqua em áreas remotas, desassistidas pelas operadoras ou zonas rurais. Contudo, manter a continuidade do serviço em redes UAV, especialmente durante o handover, é mais desafiador que nas redes terrestres. Isso ocorre devido à menor cobertura da rede, aumentando o risco de efeito ping-pong. Portanto, este artigo propõe uma solução baseada em aprendizado por reforço, que utiliza contexto de mobilidade do usuário e da rede. Os resultados indicam a eficácia da proposta, com uma redução de 74% nas falhas de handover em comparação com soluções da literatura.Referências
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Queiroz, A., Barbosa, M. K., and Dias, K. (2023). Aero5gbs—deep learning-empowered ground users handover in aerial 5g and beyond systems. IEEE Access.
Tafintsev, N., Chiumento, A., Vikhrova, O., Valkama, M., and Andreev, S. (2023). Utilization of uavs as flying base stations in urban environments. In 2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops.
Yang, H., Hu, B., and Wang, L. (2017). A deep learning based handover mechanism for UAV networks. In 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE.
Aydin, Y., Kurt, G. K., Ozdemir, E., and Yanikomeroglu, H. (2021). Group handover for drone base stations. IEEE Internet of Things Journal, 8(18):13876–13887.
Derhab, A., Cheikhrouhou, O., Allouch, A., Koubaa, A., Qureshi, B., Ferrag, M. A., Maglaras, L., and Khan, F. A. (2023). Internet of drones security: Taxonomies, open issues, and future directions. in Vehicular Communications.
Gangula, R., Esrafilian, O., Gesbert, D., Roux, C., Kaltenberger, F., and Knopp, R. (2018). Flying rebots: First results on an autonomous uav-based lte relay using open airinterface. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications.
Hasselt, H. v., Guez, A., and Silver, D. (2016). Deep reinforcement learning with double q-learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16, page 2094–2100. AAAI Press.
Hu, H., Yang, L., and Wang, S. (2019). A trajectory prediction based intelligent handover control method in UAV cellular networks. China Communications, 16(1):1–14.
Jang, Y., Raza, S. M., Kim, M., and Choo, H. (2022). Proactive handover decision for uavs with deep reinforcement learning. Sensors, 22(3).
Lin, X. (2022). An overview of 5g advanced evolution in 3gpp release 18. IEEE Communications Standards Magazine, 6(3):77–83.
Mishra, D. and Natalizio, E. (2020). A survey on cellular-connected uavs: Design challenges, enabling 5g/b5g innovations, and experimental advancements. Computer Networks.
Muruganathan, S. D., Lin, X., Määttänen, H.-L., Sedin, J., Zou, Z., Hapsari, W. A., and Yasukawa, S. (2021). An overview of 3gpp release-15 study on enhanced lte support for connected drones. IEEE Communications Standards Magazine, 5(4):140–146.
Queiroz, A., Barbosa, M. K., and Dias, K. (2023). Aero5gbs—deep learning-empowered ground users handover in aerial 5g and beyond systems. IEEE Access.
Tafintsev, N., Chiumento, A., Vikhrova, O., Valkama, M., and Andreev, S. (2023). Utilization of uavs as flying base stations in urban environments. In 2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops.
Yang, H., Hu, B., and Wang, L. (2017). A deep learning based handover mechanism for UAV networks. In 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE.
Publicado
21/07/2024
Como Citar
BARBOSA, Maria; BATISTA, Marcelo; QUEIROZ, Anderson; CAVALCANTI, David; DIAS, Kelvin.
Handover em redes aéreas 5G: Uma solução baseada em aprendizado por reforço. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 51. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 276-287.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2024.3152.
