IDEAS home Printed from https://ideas.repec.org/a/pkp/rocere/v5y2018i2p49-56id1460.html
   My bibliography  Save this article

Characterisation of Propagation Loss for a 3G Cellular Network in a Crowded Market Area Using CCIR Model

Author

Listed:
  • Simeon Ozuomba
  • Enyenihi Johnson
  • Ngwu Chinyere Rosemary

Abstract

In this paper, the propagation loss for 1800 MHz cellular network in a crowded market is studied and characterized using the Comit´e International des Radio-Communication, (CCIR) propagation loss model. Empirical measurement of the received signal strength in the market was conducted using CellMapper android app installed on Samsung Galaxy S4 phone. The CCIR model was configured with three different percentages of covered areas (PB). The model was optimized using the root means square error (RMSE) method and also by tuning the PB value. The un-tuned CCIR model gave an RMSE value of 9.23 dB which is above the acceptable upper limit of 6 dB for propagation loss prediction models. On the other hand, the PB-tuned CCIR model gave the best prediction result with an RMSE value of 2.177 dB and prediction accuracy of 98.11 % which is better than the performance of all the RMSE-tuned CCIR models. The results showed that apart from using an RMSE value to tune the CCIR propagation loss model, adjustment of some other key parameters of the model can as well provide a better prediction performance. However, the choice of the parameter to be tuned depends on the specific nature of the case study area.

Suggested Citation

  • Simeon Ozuomba & Enyenihi Johnson & Ngwu Chinyere Rosemary, 2018. "Characterisation of Propagation Loss for a 3G Cellular Network in a Crowded Market Area Using CCIR Model," Review of Computer Engineering Research, Conscientia Beam, vol. 5(2), pages 49-56.
  • Handle: RePEc:pkp:rocere:v:5:y:2018:i:2:p:49-56:id:1460
    as

    Download full text from publisher

    File URL: https://archive.conscientiabeam.com/index.php/76/article/view/1460/2041
    Download Restriction: no

    File URL: https://archive.conscientiabeam.com/index.php/76/article/view/1460/4772
    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:pkp:rocere:v:5:y:2018:i:2:p:49-56:id:1460. 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: Dim Michael (email available below). General contact details of provider: https://archive.conscientiabeam.com/index.php/76/ .

    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.