American Journal of Software Engineering and Applications

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Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

Received: 08 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.

DOI 10.11648/j.ajsea.20170603.11
Published in American Journal of Software Engineering and Applications (Volume 6, Issue 3, June 2017)
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), 2024. 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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Author Information
  • Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria

  • Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria

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

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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://download.sciencepg.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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