Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond-Reference-Cited by-同舟云学术

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Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond

Published:2024-08-29 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Marenco Ludwing,Hupalo Luiz E.,Andrade Naylson F.,de Figueiredo Felipe A. P.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Ministério da Ciência, Tecnologia, Inovações e Comunicações

Empresa Brasileira de Pesquisa e Inovação Industrial

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-70651-9.pdf

Reference39 articles.

1. Dala Pegorara Souto, V. et al. Emerging mimo technologies for 6g networks. Sensors. https://doi.org/10.3390/s23041921 (2023).

2. Giannoulis, S. et al. Dynamic and collaborative spectrum sharing: The scatter approach. in 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 1–6. https://doi.org/10.1109/DySPAN.2019.8935774 (2019).

3. Niu, Y., Li, Y., Jin, D., Su, L. & Vasilakos, A. V. A survey of millimeter wave communications (mmWave) for 5g: Opportunities and challenges. Wireless Netw. 21, 2657–2676. https://doi.org/10.1007/s11276-015-0942-z (2015).

4. Sim, M. S., Lim, Y., Park, S. H., Dai, L. & Chae, C. Deep learning-based mmWave beam selection for 5G NR/6G with sub-6 GHz channel information: Algorithms and prototype validation. IEEE Access 8, 51634–51646. https://doi.org/10.1109/ACCESS.2020.2980285 (2020).

5. Brilhante, D. S. et al. A literature survey on AI-aided beamforming and beam management for 5g and 6g systems. Sensors. https://doi.org/10.3390/s23094359 (2023).

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