IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i24p3985-d1546925.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Julio Ernesto Zaldivar-Herrera & Luis Pastor Sánchez-Fernández & Luis Manuel Rodríguez-Méndez, 2024. "Network Long-Term Evolution Quality of Service Assessment Using a Weighted Fuzzy Inference System," Mathematics, MDPI, vol. 12(24), pages 1-26, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:3985-:d:1546925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/24/3985/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/24/3985/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agbotiname Lucky Imoize & Friday Udeji & Joseph Isabona & Cheng-Chi Lee, 2023. "Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment," Future Internet, MDPI, vol. 15(5), pages 1-35, May.
    2. Roseline Oluwaseun Ogundokun & Joseph Bamidele Awotunde & Agbotiname Lucky Imoize & Chun-Ta Li & AbdulRahman Tosho Abdulahi & Abdulwasiu Bolakale Adelodun & Samarendra Nath Sur & Cheng-Chi Lee, 2023. "Non-Orthogonal Multiple Access Enabled Mobile Edge Computing in 6G Communications: A Systematic Literature Review," Sustainability, MDPI, vol. 15(9), pages 1-34, April.
    3. Alberto D. Dávila-Lamas & José J. Carbajal-Hernández & Luis P. Sánchez-Fernández & Virginia B. Niebla-Zatarain & César A. Hoil-Rosas, 2022. "Assessment of Coastal Locations Safety Using a Fuzzy Analytical Hierarchy Process-Based Model," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huu Q. Tran & Samarendra Nath Sur & Byung Moo Lee, 2024. "A Comprehensive Analytical Framework under Practical Constraints for a Cooperative NOMA System Empowered by SWIPT IoT," Mathematics, MDPI, vol. 12(14), pages 1-25, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:3985-:d:1546925. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.