Learning to Solve Decision Problems Over Two Timescales: An Application to 5G Puncturing
Author:
Funder
Agencia Nacional de Investigación e Innovación
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10735-3.pdf
Reference34 articles.
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2. Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine learning for 5g/b5g mobile and wireless communications: Potential, limitations, and future directions. IEEE Access, 7, 137184–137206.
3. Klautau, A., Batista, P., González-Prelcic, N., Wang, Y., & Heath, R.W. (2018) 5g mimo data for machine learning: Application to beam-selection using deep learning. In: 2018 Information Theory and Applications Workshop (ITA), pp. 1–9. IEEE
4. Huang, H., Guo, S., Gui, G., Yang, Z., Zhang, J., Sari, H., & Adachi, F. (2019). Deep learning for physical-layer 5g wireless techniques: Opportunities, challenges and solutions. IEEE Wireless Communications, 27(1), 214–222.
5. Hasan, M.K., Shahjalal, M., Islam, M.M., Alam, M.M., Ahmed, M.F., & Jang, Y.M. (2020) The role of deep learning in noma for 5g and beyond communications. In: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 303–307. IEEE
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