Optimization of Hata Pathloss Model Using Terrain Roughness Parameter

Fidelis Osanebi Chucks Nwaduwa; Wali Samuel; Asuquo Ifiok Okon

doi:doi:10.11648/j.se.20170503.12

| Peer-Reviewed

Optimization of Hata Pathloss Model Using Terrain Roughness Parameter

Fidelis Osanebi Chucks Nwaduwa, Wali Samuel, Asuquo Ifiok Okon

Published in Software Engineering (Volume 5, Issue 3)

Received: 3 January 2017 Accepted: 10 January 2017 Published: 29 August 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In this paper, an approach for optimizing Hata pathloss model based on terrain roughness parameter is presented. The study is based on field measurement of received signal strength and elevation profile obtained in a suburban area for a GSM network in the 800 MHz frequency band. Mostly, standard deviation of elevation is used to characterize terrain roughness. However, in this paper, the mean elevation and the standard deviation of elevation are used separately to minimize the error using least square method. The results show that the untuned Hata model has a RMSE of 44.58 dB and prediction accuracy of 65.07%. On the other hand, both the pathloss predicted by the mean elevation tuned Hata model and the pathloss predicted by the standard deviation of elevation tuned Hata model have the same RME of 6.23 dB and prediction accuracy of 96.06%. Also, the terrain roughness correction factors are the same value (that is, C_TSDV=C_TMean=44.13848). Finally, with the RMSE of about 6 dB, it can be concluded that the terrain roughness parameter-based tuning approach can effectively be used to minimize the prediction error of the Hata model within the acceptable value which is about 7dB to 10 dB for urban and rural areas.

Published in	Software Engineering (Volume 5, Issue 3)
DOI	10.11648/j.se.20170503.12
Page(s)	51-56
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

Hata Model, Pathloss, Terrain Roughness Parameter, Least Square Method, Empirical Pathos Model

References

[1]	Ranvier, S. (2004). Path loss models. Helsinki University of Technology.
[2]	Roslee, M. B., & Kwan, K. F. (2010). Optimization of Hata propagation prediction model in suburban area in Malaysia. Progress In Electromagnetics Research C, 13, 91-106.
[3]	Singh, Y. (2012). Comparison of Okumura, Hata and COST-231 Models on the Basis of Path Loss and Signal Strength. International Journal of Computer Applications, 59 (11).
[4]	Randeep S. C., Yuvraj S., Sandeep S. and Rakesh G., (2015) Performance & Evaluation of Propagation Models for Sub-Urban Areas. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Vol. 4, Issue 2, February 2015.
[5]	Nadir, Z., Elfadhil, N., & Touati, F. (2008, July). Pathloss determination using Okumura-Hata model and spline interpolation for missing data for Oman. In Proceedings of the world congress on Engineering (Vol. 1, pp. 2-4).
[6]	Abhayawardhana, V. S., Wassell, I. J., Crosby, D., Sellars, M. P., & Brown, M. G. (2005, May). Comparison of empirical propagation path loss models for fixed wireless access systems. In 2005 IEEE 61st Vehicular Technology Conference (Vol. 1, pp. 73-77). IEEE.
[7]	Alumona, T. L. (2015). Path Loss Prediction of Wireless Mobile Communication for Urban Areas of Imo State, South-East Region of Nigeria at 910 MHz. International Journal of Sensor Networks and Data Communications, 2015.
[8]	Thomas, T., & Vivek, M. V. (2015). Path loss Determination Using Hata Model and Effect of Path loss in OFDM. environment, 1 (8).
[9]	Ekka, A. (2012). Pathloss Determination Using Okumura-hata Model for Rourkela (Doctoral dissertation, National Institute of Technology Rourkela).
[10]	Verma, R., & Saini, G. (2015). DIFFERENT PROPAGATION MODELLING TOOLS USED FOR VARIOUS INDOOR AND OUTDOOR SCENARIOS.
[11]	Phillips, C., Sicker, D., & Grunwald, D. (2012). Bounding the practical error of path loss models. International Journal of Antennas and Propagation, 2012.
[12]	Bhuvaneshwari, A., Hemalatha, R., & Satyasavithri, T. (2013, October). Statistical tuning of the best suited prediction model for measurements made in Hyderabad city of Southern India. In Proceedings of the world congress on engineering and computer science (Vol. 2, pp. 23-25).
[13]	Keawbunsong, P., Supannakoon, P., & Promwong, S. (2015). Optimized Walficsh-Bertoni Model for Path Loss Prediction DTTV Propagation in Urban Area of Southern Thailand. Advanced Science Letters, 21 (10), 3029-3032.
[14]	Keawbunsong, P., Supannakoon, P., & Promwong, S. (2015). Optimization of Path Loss Model for Prediction DTTV Propagation in Urban Area of Southern Thailand. Advanced Science Letters, 21 (10), 3064-3068.
[15]	Diawuo, K., Dotche, K. A., & Cumberbatch, T. (2013). Data Fitting to Propagation Model Using Least Square Algorithm: A Case Study in Ghana.
[16]	Mousa, A., Dama, Y., Najjar, M., & Alsayeh, B. (2012). Optimizing Outdoor Propagation Model based on Measurements for Multiple RF Cell. International Journal of Computer Applications, 60 (5).
[17]	Roslee, M. B., & Kwan, K. F. (2010). Optimization of Hata propagation prediction model in suburban area in Malaysia. Progress In Electromagnetics Research C, 13, 91-106.

Cite This Article

Plain Text BibTeX RIS

APA Style

Fidelis Osanebi Chucks Nwaduwa, Wali Samuel, Asuquo Ifiok Okon. (2017). Optimization of Hata Pathloss Model Using Terrain Roughness Parameter. Software Engineering, 5(3), 51-56. https://doi.org/10.11648/j.se.20170503.12

Copy | Download

ACS Style

Fidelis Osanebi Chucks Nwaduwa; Wali Samuel; Asuquo Ifiok Okon. Optimization of Hata Pathloss Model Using Terrain Roughness Parameter. Softw. Eng. 2017, 5(3), 51-56. doi: 10.11648/j.se.20170503.12

Copy | Download

AMA Style

Fidelis Osanebi Chucks Nwaduwa, Wali Samuel, Asuquo Ifiok Okon. Optimization of Hata Pathloss Model Using Terrain Roughness Parameter. Softw Eng. 2017;5(3):51-56. doi: 10.11648/j.se.20170503.12

Copy | Download

@article{10.11648/j.se.20170503.12,
  author = {Fidelis Osanebi Chucks Nwaduwa and Wali Samuel and Asuquo Ifiok Okon},
  title = {Optimization of Hata Pathloss Model Using Terrain Roughness Parameter},
  journal = {Software Engineering},
  volume = {5},
  number = {3},
  pages = {51-56},
  doi = {10.11648/j.se.20170503.12},
  url = {https://doi.org/10.11648/j.se.20170503.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20170503.12},
  abstract = {In this paper, an approach for optimizing Hata pathloss model based on terrain roughness parameter is presented. The study is based on field measurement of received signal strength and elevation profile obtained in a suburban area for a GSM network in the 800 MHz frequency band. Mostly, standard deviation of elevation is used to characterize terrain roughness. However, in this paper, the mean elevation and the standard deviation of elevation are used separately to minimize the error using least square method. The results show that the untuned Hata model has a RMSE of 44.58 dB and prediction accuracy of 65.07%. On the other hand, both the pathloss predicted by the mean elevation tuned Hata model and the pathloss predicted by the standard deviation of elevation tuned Hata model have the same RME of 6.23 dB and prediction accuracy of 96.06%. Also, the terrain roughness correction factors are the same value (that is, CTSDV=CTMean=44.13848). Finally, with the RMSE of about 6 dB, it can be concluded that the terrain roughness parameter-based tuning approach can effectively be used to minimize the prediction error of the Hata model within the acceptable value which is about 7dB to 10 dB for urban and rural areas.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Optimization of Hata Pathloss Model Using Terrain Roughness Parameter
AU  - Fidelis Osanebi Chucks Nwaduwa
AU  - Wali Samuel
AU  - Asuquo Ifiok Okon
Y1  - 2017/08/29
PY  - 2017
N1  - https://doi.org/10.11648/j.se.20170503.12
DO  - 10.11648/j.se.20170503.12
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 51
EP  - 56
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20170503.12
AB  - In this paper, an approach for optimizing Hata pathloss model based on terrain roughness parameter is presented. The study is based on field measurement of received signal strength and elevation profile obtained in a suburban area for a GSM network in the 800 MHz frequency band. Mostly, standard deviation of elevation is used to characterize terrain roughness. However, in this paper, the mean elevation and the standard deviation of elevation are used separately to minimize the error using least square method. The results show that the untuned Hata model has a RMSE of 44.58 dB and prediction accuracy of 65.07%. On the other hand, both the pathloss predicted by the mean elevation tuned Hata model and the pathloss predicted by the standard deviation of elevation tuned Hata model have the same RME of 6.23 dB and prediction accuracy of 96.06%. Also, the terrain roughness correction factors are the same value (that is, CTSDV=CTMean=44.13848). Finally, with the RMSE of about 6 dB, it can be concluded that the terrain roughness parameter-based tuning approach can effectively be used to minimize the prediction error of the Hata model within the acceptable value which is about 7dB to 10 dB for urban and rural areas.
VL  - 5
IS  - 3
ER  -

Copy | Download

Author Information

Fidelis Osanebi Chucks Nwaduwa

Department of Electrical/Computer Engineering, Port Harcourt Polytechnic, Rumuola, Port Harcourt, Nigeria
Wali Samuel

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
Asuquo Ifiok Okon

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Fidelis Osanebi Chucks Nwaduwa, Wali Samuel, Asuquo Ifiok Okon. (2017). Optimization of Hata Pathloss Model Using Terrain Roughness Parameter. Software Engineering, 5(3), 51-56. https://doi.org/10.11648/j.se.20170503.12

Copy | Download

ACS Style

Fidelis Osanebi Chucks Nwaduwa; Wali Samuel; Asuquo Ifiok Okon. Optimization of Hata Pathloss Model Using Terrain Roughness Parameter. Softw. Eng. 2017, 5(3), 51-56. doi: 10.11648/j.se.20170503.12

Copy | Download

AMA Style

Fidelis Osanebi Chucks Nwaduwa, Wali Samuel, Asuquo Ifiok Okon. Optimization of Hata Pathloss Model Using Terrain Roughness Parameter. Softw Eng. 2017;5(3):51-56. doi: 10.11648/j.se.20170503.12

Copy | Download

@article{10.11648/j.se.20170503.12,
  author = {Fidelis Osanebi Chucks Nwaduwa and Wali Samuel and Asuquo Ifiok Okon},
  title = {Optimization of Hata Pathloss Model Using Terrain Roughness Parameter},
  journal = {Software Engineering},
  volume = {5},
  number = {3},
  pages = {51-56},
  doi = {10.11648/j.se.20170503.12},
  url = {https://doi.org/10.11648/j.se.20170503.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20170503.12},
  abstract = {In this paper, an approach for optimizing Hata pathloss model based on terrain roughness parameter is presented. The study is based on field measurement of received signal strength and elevation profile obtained in a suburban area for a GSM network in the 800 MHz frequency band. Mostly, standard deviation of elevation is used to characterize terrain roughness. However, in this paper, the mean elevation and the standard deviation of elevation are used separately to minimize the error using least square method. The results show that the untuned Hata model has a RMSE of 44.58 dB and prediction accuracy of 65.07%. On the other hand, both the pathloss predicted by the mean elevation tuned Hata model and the pathloss predicted by the standard deviation of elevation tuned Hata model have the same RME of 6.23 dB and prediction accuracy of 96.06%. Also, the terrain roughness correction factors are the same value (that is, CTSDV=CTMean=44.13848). Finally, with the RMSE of about 6 dB, it can be concluded that the terrain roughness parameter-based tuning approach can effectively be used to minimize the prediction error of the Hata model within the acceptable value which is about 7dB to 10 dB for urban and rural areas.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Optimization of Hata Pathloss Model Using Terrain Roughness Parameter
AU  - Fidelis Osanebi Chucks Nwaduwa
AU  - Wali Samuel
AU  - Asuquo Ifiok Okon
Y1  - 2017/08/29
PY  - 2017
N1  - https://doi.org/10.11648/j.se.20170503.12
DO  - 10.11648/j.se.20170503.12
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 51
EP  - 56
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20170503.12
AB  - In this paper, an approach for optimizing Hata pathloss model based on terrain roughness parameter is presented. The study is based on field measurement of received signal strength and elevation profile obtained in a suburban area for a GSM network in the 800 MHz frequency band. Mostly, standard deviation of elevation is used to characterize terrain roughness. However, in this paper, the mean elevation and the standard deviation of elevation are used separately to minimize the error using least square method. The results show that the untuned Hata model has a RMSE of 44.58 dB and prediction accuracy of 65.07%. On the other hand, both the pathloss predicted by the mean elevation tuned Hata model and the pathloss predicted by the standard deviation of elevation tuned Hata model have the same RME of 6.23 dB and prediction accuracy of 96.06%. Also, the terrain roughness correction factors are the same value (that is, CTSDV=CTMean=44.13848). Finally, with the RMSE of about 6 dB, it can be concluded that the terrain roughness parameter-based tuning approach can effectively be used to minimize the prediction error of the Hata model within the acceptable value which is about 7dB to 10 dB for urban and rural areas.
VL  - 5
IS  - 3
ER  -

Copy | Download