Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

Nnadi Nathaniel Chimaobi; Ifeanyi Chima Nnadi; Chibuzo Promise Nkwocha

doi:doi:10.11648/j.com.20170503.11

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Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha

Published in Communications (Volume 5, Issue 3)

Received: 8 January 2017 Accepted: 24 January 2017 Published: 14 June 2017

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Abstract

Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both 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	Communications (Volume 5, Issue 3)
DOI	10.11648/j.com.20170503.11
Page(s)	19-23
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, Propagation Model, CCIR Model, Composition Function, Empirical Model, RMSE-Based Tuning Approach, Least Square Method

References

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APA Style

Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha. (2017). Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model. Communications, 5(3), 19-23. https://doi.org/10.11648/j.com.20170503.11

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ACS Style

Nnadi Nathaniel Chimaobi; Ifeanyi Chima Nnadi; Chibuzo Promise Nkwocha. Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model. Communications. 2017, 5(3), 19-23. doi: 10.11648/j.com.20170503.11

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AMA Style

Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha. Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model. Communications. 2017;5(3):19-23. doi: 10.11648/j.com.20170503.11

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@article{10.11648/j.com.20170503.11,
  author = {Nnadi Nathaniel Chimaobi and Ifeanyi Chima Nnadi and Chibuzo Promise Nkwocha},
  title = {Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model},
  journal = {Communications},
  volume = {5},
  number = {3},
  pages = {19-23},
  doi = {10.11648/j.com.20170503.11},
  url = {https://doi.org/10.11648/j.com.20170503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20170503.11},
  abstract = {Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both 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 CCIR Pathloss Model
AU  - Nnadi Nathaniel Chimaobi
AU  - Ifeanyi Chima Nnadi
AU  - Chibuzo Promise Nkwocha
Y1  - 2017/06/14
PY  - 2017
N1  - https://doi.org/10.11648/j.com.20170503.11
DO  - 10.11648/j.com.20170503.11
T2  - Communications
JF  - Communications
JO  - Communications
SP  - 19
EP  - 23
PB  - Science Publishing Group
SN  - 2328-5923
UR  - https://doi.org/10.11648/j.com.20170503.11
AB  - Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both 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  - 5
IS  - 3
ER  -

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Author Information

Nnadi Nathaniel Chimaobi

Department of Electrical/Electronic Engineering, Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Ifeanyi Chima Nnadi

Department of Electrical/Electronic Engineering, Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Chibuzo Promise Nkwocha

Department of Chemical Engineering, Federal University of Technology, Owerri (FUTO), Owerri, Nigeria

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Plain Text BibTeX RIS

APA Style

Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha. (2017). Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model. Communications, 5(3), 19-23. https://doi.org/10.11648/j.com.20170503.11

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ACS Style

Nnadi Nathaniel Chimaobi; Ifeanyi Chima Nnadi; Chibuzo Promise Nkwocha. Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model. Communications. 2017, 5(3), 19-23. doi: 10.11648/j.com.20170503.11

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AMA Style

Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha. Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model. Communications. 2017;5(3):19-23. doi: 10.11648/j.com.20170503.11

Copy | Download

@article{10.11648/j.com.20170503.11,
  author = {Nnadi Nathaniel Chimaobi and Ifeanyi Chima Nnadi and Chibuzo Promise Nkwocha},
  title = {Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model},
  journal = {Communications},
  volume = {5},
  number = {3},
  pages = {19-23},
  doi = {10.11648/j.com.20170503.11},
  url = {https://doi.org/10.11648/j.com.20170503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20170503.11},
  abstract = {Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both 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 CCIR Pathloss Model
AU  - Nnadi Nathaniel Chimaobi
AU  - Ifeanyi Chima Nnadi
AU  - Chibuzo Promise Nkwocha
Y1  - 2017/06/14
PY  - 2017
N1  - https://doi.org/10.11648/j.com.20170503.11
DO  - 10.11648/j.com.20170503.11
T2  - Communications
JF  - Communications
JO  - Communications
SP  - 19
EP  - 23
PB  - Science Publishing Group
SN  - 2328-5923
UR  - https://doi.org/10.11648/j.com.20170503.11
AB  - Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both 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  - 5
IS  - 3
ER  -

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