IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v17y2025i1p46-d1570919.html
   My bibliography  Save this article

Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications

Author

Listed:
  • Teguh Indra Bayu

    (Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia)

  • Yung-Fa Huang

    (Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Jeang-Kuo Chen

    (Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Cheng-Hsiung Hsieh

    (Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Budhi Kristianto

    (Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia)

  • Erwien Christianto

    (Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia)

  • Suharyadi Suharyadi

    (Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia)

Abstract

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ( P r k ) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for P r k and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.

Suggested Citation

  • Teguh Indra Bayu & Yung-Fa Huang & Jeang-Kuo Chen & Cheng-Hsiung Hsieh & Budhi Kristianto & Erwien Christianto & Suharyadi Suharyadi, 2025. "Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications," Future Internet, MDPI, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:1:p:46-:d:1570919
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/17/1/46/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/17/1/46/
    Download Restriction: no
    ---><---

    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:jftint:v:17:y:2025:i:1:p:46-:d:1570919. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.