Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method

Yiğit Kaan Bozkurt; Ibraheem Shayea; Abdulraqeb Alhammadi; Zhetkerbay Askhat

doi:10.1007/978-3-032-00232-7_48

Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method

Yiğit Kaan Bozkurt
, Ibraheem Shayea^*
, Abdulraqeb Alhammadi
, Zhetkerbay Askhat

^*Corresponding author for this work

Faculty of Electrical and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study explores the relationship between user density and the performance of fifth generation (5G) networks, focusing on latency behavior within a specific region using a large internet dataset. The analysis employs clustering techniques to determine network density and statistical modeling to evaluate latency dynamics. Results highlight a weak and statistically insignificant correlation between user density and latency, influenced by clustering methodology, dataset scope, and advancements in 5G traffic management technologies. The findings emphasize the importance of incorporating diverse and larger datasets, along with more sophisticated clustering methodologies, to accurately evaluate and enhance 5G network performance, particularly under conditions of varying user density.

Original language	English
Title of host publication	Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations
Editors	Ali Othman Albaji
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	736-750
Number of pages	15
ISBN (Print)	9783032002310
DOIs	https://doi.org/10.1007/978-3-032-00232-7_48
Publication status	Published - 2026
Event	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 - Tripoli, Libya Duration: 9 Jul 2025 → 10 Jul 2025

Publication series

Name	Studies in Computational Intelligence
Volume	1229 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025
Country/Territory	Libya
City	Tripoli
Period	9/07/25 → 10/07/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

5G
Clustering
Mobile Broadband
Statistical Analysis

Access to Document

10.1007/978-3-032-00232-7_48

Cite this

Bozkurt, Y. K., Shayea, I., Alhammadi, A., & Askhat, Z. (2026). Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method. In A. O. Albaji (Ed.), Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations (pp. 736-750). (Studies in Computational Intelligence; Vol. 1229 SCI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-00232-7_48

Bozkurt, Yiğit Kaan ; Shayea, Ibraheem ; Alhammadi, Abdulraqeb et al. / Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method. Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. editor / Ali Othman Albaji. Springer Science and Business Media Deutschland GmbH, 2026. pp. 736-750 (Studies in Computational Intelligence).

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title = "Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method",

abstract = "This study explores the relationship between user density and the performance of fifth generation (5G) networks, focusing on latency behavior within a specific region using a large internet dataset. The analysis employs clustering techniques to determine network density and statistical modeling to evaluate latency dynamics. Results highlight a weak and statistically insignificant correlation between user density and latency, influenced by clustering methodology, dataset scope, and advancements in 5G traffic management technologies. The findings emphasize the importance of incorporating diverse and larger datasets, along with more sophisticated clustering methodologies, to accurately evaluate and enhance 5G network performance, particularly under conditions of varying user density.",

keywords = "5G, Clustering, Mobile Broadband, Statistical Analysis",

author = "Bozkurt, \{Yiğit Kaan\} and Ibraheem Shayea and Abdulraqeb Alhammadi and Zhetkerbay Askhat",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 ; Conference date: 09-07-2025 Through 10-07-2025",

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doi = "10.1007/978-3-032-00232-7\_48",

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series = "Studies in Computational Intelligence",

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Bozkurt, YK, Shayea, I, Alhammadi, A & Askhat, Z 2026, Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method. in AO Albaji (ed.), Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. Studies in Computational Intelligence, vol. 1229 SCI, Springer Science and Business Media Deutschland GmbH, pp. 736-750, International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025, Tripoli, Libya, 9/07/25. https://doi.org/10.1007/978-3-032-00232-7_48

Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method. / Bozkurt, Yiğit Kaan; Shayea, Ibraheem; Alhammadi, Abdulraqeb et al.
Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. ed. / Ali Othman Albaji. Springer Science and Business Media Deutschland GmbH, 2026. p. 736-750 (Studies in Computational Intelligence; Vol. 1229 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bozkurt, Yiğit Kaan

AU - Shayea, Ibraheem

AU - Alhammadi, Abdulraqeb

AU - Askhat, Zhetkerbay

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - This study explores the relationship between user density and the performance of fifth generation (5G) networks, focusing on latency behavior within a specific region using a large internet dataset. The analysis employs clustering techniques to determine network density and statistical modeling to evaluate latency dynamics. Results highlight a weak and statistically insignificant correlation between user density and latency, influenced by clustering methodology, dataset scope, and advancements in 5G traffic management technologies. The findings emphasize the importance of incorporating diverse and larger datasets, along with more sophisticated clustering methodologies, to accurately evaluate and enhance 5G network performance, particularly under conditions of varying user density.

AB - This study explores the relationship between user density and the performance of fifth generation (5G) networks, focusing on latency behavior within a specific region using a large internet dataset. The analysis employs clustering techniques to determine network density and statistical modeling to evaluate latency dynamics. Results highlight a weak and statistically insignificant correlation between user density and latency, influenced by clustering methodology, dataset scope, and advancements in 5G traffic management technologies. The findings emphasize the importance of incorporating diverse and larger datasets, along with more sophisticated clustering methodologies, to accurately evaluate and enhance 5G network performance, particularly under conditions of varying user density.

KW - 5G

KW - Clustering

KW - Mobile Broadband

KW - Statistical Analysis

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BT - Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations

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Bozkurt YK, Shayea I, Alhammadi A, Askhat Z. Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method. In Albaji AO, editor, Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. Springer Science and Business Media Deutschland GmbH. 2026. p. 736-750. (Studies in Computational Intelligence). doi: 10.1007/978-3-032-00232-7_48

Statistical Analysis of 5G Network Latency Based on Density-Based Clustering Method

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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