IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v88y2025i3d10.1007_s11235-025-01314-x.html
   My bibliography  Save this article

ML Algorithms Analysis and Prediction of Broadband Electric Field Levels in Telecommunication Systems Environment

Author

Listed:
  • Doruntinë Berisha

    (University of Prishtina)

  • Arjeta Jerliu

    (University of Prishtina)

  • Mimoza Ibrani

    (University of Prishtina)

Abstract

This study investigates artificial intelligence neural networks to analyze and predict sub-3 GHz electric field exposure levels. The research encompasses a diverse set of environments including urban areas, residential zones, public transportation settings, and office spaces, which collectively represent realistic exposure environments for radio frequency electromagnetic field emissions. Empirical data, acquired from multiple urban and indoor settings, underpin the development of predictive models. The predictive models are developed using artificial neural network methodologies, specifically the Generalized Regression Neural Network and Radial Basis Function Neural Network. The study presents a detailed assessment of the simulation results, highlighting the effectiveness of these artificial neural network-based approaches in predicting electric field levels across varied environmental conditions. While optimized for broadband exposure, the models may not generalize to millimeter-wave frequencies, which behave differently in terms of propagation and penetration. The findings emphasize the potential of Generalized Regression and Radial Basis Function neural networks for accurate and reliable prediction of radio frequency electromagnetic field levels.

Suggested Citation

  • Doruntinë Berisha & Arjeta Jerliu & Mimoza Ibrani, 2025. "ML Algorithms Analysis and Prediction of Broadband Electric Field Levels in Telecommunication Systems Environment," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(3), pages 1-14, September.
  • Handle: RePEc:spr:telsys:v:88:y:2025:i:3:d:10.1007_s11235-025-01314-x
    DOI: 10.1007/s11235-025-01314-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-025-01314-x
    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/s11235-025-01314-x?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. Shanshan Wang & Joe Wiart, 2020. "Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks," IJERPH, MDPI, vol. 17(9), pages 1-15, April.
    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. Malka N. Halgamuge, 2020. "Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells," IJERPH, MDPI, vol. 17(12), pages 1-27, June.
    2. Teruo Onishi & Miwa Ikuyo & Kazuhiro Tobita & Sen Liu & Masao Taki & Soichi Watanabe, 2021. "Radiofrequency Exposure Levels from Mobile Phone Base Stations in Outdoor Environments and an Underground Shopping Mall in Japan," IJERPH, MDPI, vol. 18(15), pages 1-10, 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:spr:telsys:v:88:y:2025:i:3:d:10.1007_s11235-025-01314-x. 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.