Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

Nnadi Nathaniel Chimaobi; Charles Chukwuemeka Nnadi; Amaechi Justice Nzegwu

doi:doi:10.11648/j.ajsea.20170603.11

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Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

Nnadi Nathaniel Chimaobi, Charles Chukwuemeka Nnadi, Amaechi Justice Nzegwu

Published in American Journal of Software Engineering and Applications (Volume 6, Issue 3)

Received: 8 January 2017 Accepted: 18 January 2017 Published: 12 June 2017

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Abstract

In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.

Published in	American Journal of Software Engineering and Applications (Volume 6, Issue 3)
DOI	10.11648/j.ajsea.20170603.11
Page(s)	61-66
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Pathloss, Erceg Model, Least Square Method, Propagation Model, Model Tuning, Composition Function Of Residue

References

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APA Style

Nnadi Nathaniel Chimaobi, Charles Chukwuemeka Nnadi, Amaechi Justice Nzegwu. (2017). Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model. American Journal of Software Engineering and Applications, 6(3), 61-66. https://doi.org/10.11648/j.ajsea.20170603.11

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ACS Style

Nnadi Nathaniel Chimaobi; Charles Chukwuemeka Nnadi; Amaechi Justice Nzegwu. Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model. Am. J. Softw. Eng. Appl. 2017, 6(3), 61-66. doi: 10.11648/j.ajsea.20170603.11

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AMA Style

Nnadi Nathaniel Chimaobi, Charles Chukwuemeka Nnadi, Amaechi Justice Nzegwu. Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model. Am J Softw Eng Appl. 2017;6(3):61-66. doi: 10.11648/j.ajsea.20170603.11

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@article{10.11648/j.ajsea.20170603.11,
  author = {Nnadi Nathaniel Chimaobi and Charles Chukwuemeka Nnadi and Amaechi Justice Nzegwu},
  title = {Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model},
  journal = {American Journal of Software Engineering and Applications},
  volume = {6},
  number = {3},
  pages = {61-66},
  doi = {10.11648/j.ajsea.20170603.11},
  url = {https://doi.org/10.11648/j.ajsea.20170603.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20170603.11},
  abstract = {In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.},
 year = {2017}
}

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TY  - JOUR
T1  - Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model
AU  - Nnadi Nathaniel Chimaobi
AU  - Charles Chukwuemeka Nnadi
AU  - Amaechi Justice Nzegwu
Y1  - 2017/06/12
PY  - 2017
N1  - https://doi.org/10.11648/j.ajsea.20170603.11
DO  - 10.11648/j.ajsea.20170603.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 61
EP  - 66
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20170603.11
AB  - In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.
VL  - 6
IS  - 3
ER  -

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Author Information

Nnadi Nathaniel Chimaobi

Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Charles Chukwuemeka Nnadi

Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Amaechi Justice Nzegwu

Department of Science Laboratory Technology Imo State Polytechnic, Umuagwo, Owerri, Nigeria

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Plain Text BibTeX RIS

APA Style

Nnadi Nathaniel Chimaobi, Charles Chukwuemeka Nnadi, Amaechi Justice Nzegwu. (2017). Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model. American Journal of Software Engineering and Applications, 6(3), 61-66. https://doi.org/10.11648/j.ajsea.20170603.11

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ACS Style

Nnadi Nathaniel Chimaobi; Charles Chukwuemeka Nnadi; Amaechi Justice Nzegwu. Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model. Am. J. Softw. Eng. Appl. 2017, 6(3), 61-66. doi: 10.11648/j.ajsea.20170603.11

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AMA Style

Nnadi Nathaniel Chimaobi, Charles Chukwuemeka Nnadi, Amaechi Justice Nzegwu. Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model. Am J Softw Eng Appl. 2017;6(3):61-66. doi: 10.11648/j.ajsea.20170603.11

Copy | Download

@article{10.11648/j.ajsea.20170603.11,
  author = {Nnadi Nathaniel Chimaobi and Charles Chukwuemeka Nnadi and Amaechi Justice Nzegwu},
  title = {Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model},
  journal = {American Journal of Software Engineering and Applications},
  volume = {6},
  number = {3},
  pages = {61-66},
  doi = {10.11648/j.ajsea.20170603.11},
  url = {https://doi.org/10.11648/j.ajsea.20170603.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20170603.11},
  abstract = {In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model
AU  - Nnadi Nathaniel Chimaobi
AU  - Charles Chukwuemeka Nnadi
AU  - Amaechi Justice Nzegwu
Y1  - 2017/06/12
PY  - 2017
N1  - https://doi.org/10.11648/j.ajsea.20170603.11
DO  - 10.11648/j.ajsea.20170603.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 61
EP  - 66
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20170603.11
AB  - In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.
VL  - 6
IS  - 3
ER  -

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