Network Long-Term Evolution Quality of Service Assessment Using a Weighted Fuzzy Inference System

Julio Ernesto Zaldivar-Herrera, Luis Pastor Sánchez-Fernández () and Luis Manuel Rodríguez-Méndez
Additional contact information
Julio Ernesto Zaldivar-Herrera: Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, Gustavo A. Madero, Mexico City 07738, Mexico
Luis Pastor Sánchez-Fernández: Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, Gustavo A. Madero, Mexico City 07738, Mexico
Luis Manuel Rodríguez-Méndez: Escuela Superior de Ingeniería Mecánica y Eléctrica Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico

Mathematics, 2024, vol. 12, issue 24, 1-26

Abstract: The United Nations has pushed for improved mobile connectivity, ensuring that 97% of the world’s population lives within reach of a mobile cellular signal. This is within the framework of objective nine regarding industry, innovation, and infrastructure for sustainable development. The next challenge is for users to know the quality of this service. The Long-Term Evolution (LTE) network’s quality of service (QoS) is evaluated with key performance indicators (KPI) that only specialists can interpret. This work aims to assess the QoS and effectiveness of the fourth-generation (4G) LTE network using a weighted fuzzy inference system. Analytic Hierarchy Process (AHP) is integrated to rank the fuzzy rules. The KPIs that are considered for the evaluation are download speed, upload speed, latency, jitter, packet loss rate, reference received signal power (RSRP), and reference received signal quality (RSRQ). The evaluated data were collected collaboratively with end-user equipment (UEs). Different usage scenarios are contemplated to define the importance according to the positive impact of the QoS of the LTE mobile network. The advantage of the weighted fuzzy inference system concerning the fuzzy inference system is that each KPI is assigned a different weight, which implies having rules with hierarchies. In this way, the weighted fuzzy inference system provides two indices of quality and effectiveness. It can be a valuable tool for end users and regulatory bodies to identify the quality of the LTE mobile network.

Keywords: fuzzy inferences system; analytic hierarchy process; crowdsourcing; mobile network; quality of service (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/24/3985/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/24/3985/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:24:p:3985-:d:1546925

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().