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Review of Computer Engineering Research

June 2018, Volume 5, 2, pp 57-63

Stanford University Interim Propagation Loss Model for a Gmelina Arborea Tree-Lined Road

Umana, Sylvester. Isreal

,

Nnamonso O. Akpbio

,

Samuel Etaruwak Mbong

Umana, Sylvester. Isreal 1

Nnamonso O. Akpbio 2 Samuel Etaruwak Mbong 2 

  1. Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria 1

  2. Department of Computer Engineering, Sure Foundation Polytechnic Ukanafun, Akwa Ibom, Nigeria 2

Pages: 57-63

DOI: 10.18488/journal.76.2018.52.57.63

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Article History:

Received: 10 October, 2018
Revised: 16 November, 2018
Accepted: 24 December, 2018
Published: 14 January, 2019


Abstract:

In this paper, the extended Stanford University Interim (ESUI) path loss model was evaluated and optimized based on empirically measured path loss obtained along a suburban road lined with Gmelina Arborea trees. The study was for a 1800 MHz cellular network located in Orlu Imo state. The field measurement was conducted in August which is in the rainy season with the entire trees blossom with their green leaves. G-NetTrack Lite 8.0 Adroid app installed on a Samsung Galaxy S8 mobile phone was then used to capture and log the received signal strength (RSSI) in dB, the geo-coordinates of the measurement points as well as the particulars of the 3G network base station. Two datasets were captured and one f the dataset was used for the model optimization while the other dataset was used for validation of the model. For the training dataset, un-tuned ESUI model had a root mean square error (RMSE) of 46.82 dB and maximum absolute prediction error of 46.64 dB whereas the RMSE-tuned ESUI model had a RMSE of 4.094dB and maximum absolute prediction error of 3.41dB. Similarly, for the validation dataset, the un-tuned ESUI model had a RMSE of 48.37dB and maximum absolute prediction error of 48.19 dB whereas the RMSE-tuned ESUI model had a RMSE of 4.39 dB and maximum absolute prediction error of 3.0 dB. In all, the tuned ESUI model was derived and it gave good path loss prediction performance for both the training and the validation datasets.
Contribution/ Originality
The paper's primary contribution is the derivation of a root mean square error-based optimized extended Stanford University Interim (ESUI) path loss model for a suburban road lined with Gmelina Arborea trees.

Keywords:

Propagation loss, Cellular network, SUI model, RMSE-Tuned model, Extended sui model

Reference:


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Funding:

This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.

Acknowledgement:

All authors contributed equally to the conception and design of the study.

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