IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v77y2021i1d10.1007_s11235-020-00743-0.html
   My bibliography  Save this article

Estimation of $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel parameters using evolutionary algorithms

Author

Listed:
  • Carlos Paula Lemos

    (Ciência e Tecnologia do Triângulo Mineiro)

  • Antônio Cláudio Paschoarelli Veiga

    (Universidade Federal de Uberlândia)

  • Sandro Adriano Fasolo

    (Universidade Federal de São João del Rei)

Abstract

This paper proposes the use of evolutionary algorithms (EAs) to estimate the physical parameters of the generalized $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel model. The estimation of parameters is a fundamental step that allows for the statistical model to adjust to the real experimental data. The The maximum likelihood estimation (MLE) method that is traditionally used for estimating parameters of the $$\alpha -\kappa -\mu $$ α - κ - μ channel uses nonlinear numerical methods. In some cases, the use of nonlinear numerical methods may lead the MLE to make physically unacceptable estimations, or even to not be able to obtain a result. Our proposal is to innovate the existing EAs by incorporating an adaptive approach, a new mutation strategy and an adequate fitness function for the estimation of $$\alpha -\kappa -\mu $$ α - κ - μ parameters. Experimental results are presented to confirm that parameters estimated by the EAs (genetic algorithms, differential evolution algorithms, and differential evolution algorithms with an adaptive guiding mechanism) are all physically acceptable. These experiments show that the EAs outperform MLE estimation results.

Suggested Citation

  • Carlos Paula Lemos & Antônio Cláudio Paschoarelli Veiga & Sandro Adriano Fasolo, 2021. "Estimation of $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel parameters using evolutionary algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 189-211, May.
    • Handle: RePEc:spr:telsys:v:77:y:2021:i:1:d:10.1007_s11235-020-00743-0
    DOI: 10.1007/s11235-020-00743-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-020-00743-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-020-00743-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dhuha Abdulhadi Abduljabbar & Siti Zaiton Mohd Hashim & Roselina Sallehuddin, 2020. "Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(2), pages 225-252, June.
    2. Gilberto Fernandes & Joel J. P. C. Rodrigues & Luiz Fernando Carvalho & Jalal F. Al-Muhtadi & Mario Lemes Proença, 2019. "A comprehensive survey on network anomaly detection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(3), pages 447-489, March.
    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. Tomás Sureda Riera & Juan-Ramón Bermejo Higuera & Javier Bermejo Higuera & José-Javier Martínez Herraiz & Juan-Antonio Sicilia Montalvo, 2020. "Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review," Sustainability, MDPI, vol. 12(12), pages 1-45, June.
    2. Fanshun Zhang & Congdong Li & Cejun Cao & Zhiwei Zhang, 2022. "Random or preferential? Evolutionary mechanism of user behavior in co-creation community," Computational and Mathematical Organization Theory, Springer, vol. 28(2), pages 141-177, June.
    3. Mohammad Masdari & Hemn Khezri, 2021. "Towards fuzzy anomaly detection-based security: a comprehensive review," Fuzzy Optimization and Decision Making, Springer, vol. 20(1), pages 1-49, March.
    4. Xiaoping Li & Ching-Chun Chang & Yanjun Liu, 2021. "A generalized Chinese remainder theorem-based proactive multi-secret sharing scheme for global wide area network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(1), pages 49-56, September.

    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:spr:telsys:v:77:y:2021:i:1:d:10.1007_s11235-020-00743-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.