Average Localization Error Prediction for 5G Networks: An Investigation of Different Machine Learning Algorithms-Reference-Cited by-同舟云学术

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Average Localization Error Prediction for 5G Networks: An Investigation of Different Machine Learning Algorithms

Published:2024-05 Issue:1 Volume:136 Page:289-319
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Altay Osman,Erel-Özçevik Müge,Varol Altay Elif,Özçevik Yusuf

Funder

The Scientific and Technological Research Council of Turkey (TUBITAK) 1515 Frontier R&D Laboratories Support Program for BTS Advanced AI Hub: BTS Autonomous Networks and Data Innovation Lab.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11277-024-11257-2.pdf

Reference45 articles.

1. Elayoubi, S. E., Jemaa, S. B., Altman, Z., & Galindo-Serrano, A. (2019). 5G RAN slicing for verticals: Enablers and challenges. IEEE Communications Magazine, 57(1), 28–34.

2. Erel-Özçevik, M., & Canberk, B. (2019). Road to 5G reduced-latency: A software defined handover model for eMBB services. IEEE Transactions on Vehicular Technology, 68(8), 8133–8144.

3. Paek, J., Kim, J., & Govindan, R. (2010). Energy-efficient rate-adaptive GPS-based positioning for smartphones. In Proceedings of the 8th international conference on Mobile systems, applications, and services (pp. 299–314).

4. Chen, K., Tan, G., Cao, J., Lu, M., & Fan, X. (2019). Modeling and improving the energy performance of GPS receivers for location services. IEEE Sensors Journal, 20(8), 4512–4523.

5. Graf, T. (2011). Power-efficient positioning technologies for mobile devices. In Berlin University of Technology, SNET2 Seminar (Vol. 11).

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