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Evaluating Wireless Network Accessibility Performance via Clustering-Based Model: An Analytic Methodology

Evaluating Wireless Network Accessibility Performance via Clustering-Based Model: An Analytic Methodology

Yan Wang, Zhensen Wu
Copyright: © 2021 |Pages: 13
ISBN13: 9781799872979|ISBN10: 1799872971|EISBN13: 9781799872986
DOI: 10.4018/978-1-7998-7297-9.ch034
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MLA

Wang, Yan, and Zhensen Wu. "Evaluating Wireless Network Accessibility Performance via Clustering-Based Model: An Analytic Methodology." Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work, edited by Information Resources Management Association, IGI Global, 2021, pp. 687-699. https://doi.org/10.4018/978-1-7998-7297-9.ch034

APA

Wang, Y. & Wu, Z. (2021). Evaluating Wireless Network Accessibility Performance via Clustering-Based Model: An Analytic Methodology. In I. Management Association (Ed.), Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work (pp. 687-699). IGI Global. https://doi.org/10.4018/978-1-7998-7297-9.ch034

Chicago

Wang, Yan, and Zhensen Wu. "Evaluating Wireless Network Accessibility Performance via Clustering-Based Model: An Analytic Methodology." In Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work, edited by Information Resources Management Association, 687-699. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7297-9.ch034

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Abstract

Using the large amount of data collected by mobile operators to evaluate network performance and capacity is a promising approach developed in the recent last years. One of the challenge is to study network accessibility, based on statistical models and analytics. In particular, one aim is to identify when mobile network becomes congested, reducing accessibility performance for users. In this paper, a new analytic methodology to evaluate wireless network accessibility performance through traffic measurements is provided. The procedure is based on ensemble clustering of network cells and on regression models. It leads to identification of zones where the accessibility remains high. Numerical results show efficiency and relevance of the suggested methodology.

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