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Comparative Evaluation of the Pathloss Prediction Performance Hata-Okumura Pathloss Model for Urban, Suburban and Rural Areas

Received: 31 October 2016     Accepted: 5 January 2017     Published: 23 February 2017
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Abstract

In this paper, comparative evaluation of the pathloss prediction performance of the popular Hata-Okumura model for urban, suburban and rural areas is carried out. The study is based on empirical measurements conducted at Imo state university campus for 800 MHz GSM network. The prediction performance of the three categories of the Hata-Okumura model is analyzed and compared in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Prediction Accuracy (PA). The results showed that the Hata-Okumura model for the Urban area has the best prediction accuracy when not optimised but it has the lowest optimised prediction accuracy. On the other hand the Hata-Okumura model for the rural/open area has the least prediction accuracy when not optimised but it has the highest prediction accuracy when it is optimised. The results show that adoption of a model because it has the highest prediction accuracy is not the best approach to the selection of pathloss models. Rather the models should be optimised and the best optimised model should be adopted.

Published in International Journal of Systems Science and Applied Mathematics (Volume 2, Issue 1)
DOI 10.11648/j.ijssam.20170201.16
Page(s) 42-50
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, Hata-Okumura Model, Pathloss Model, Model Tuning, Least Square Method

References
[1] Hanchinal C. S. and Muralidhara K. N. (2016) A Survey on the Atmospheric Effects on Radio Pathloss in Cellular Mobile Communication System. IJCST Vo l. 7, Issue 1, Jan- March 2016.
[2] Rao, I. K., Donga, M., & Chukka, M. (2013) Design and Study of Propagation Models in Wireless Communications (GSM) using Free Space Pathloss Model and Hata-Okumura Model with GUI. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 2, Issue 12, December 2013
[3] Gupta, S. (2013, September). Comparative Pathloss Analysis Of Okumura And COST 231 Models For Wireless Mobile Communication Using MATLAB Simulation. In International Journal of Engineering Research and Technology (Vol. 2, No. 3 (March-2013)). ESRSA Publications.
[4] Alim, M. A., Rahman, M. M., Hossain, M. M., & Nahid, A. A. (2010). Analysis of Large Scale Propagation Models for Mobile Communications in Urban Area. arXiv preprint arXiv: 1002. 2187.
[5] Sharma, P. K., & Singh, R. K. (2012). Analysis of Large Scale Propagation Models & RF Coverage Estimation. Analysis, 2 (2).
[6] Sati, G., & Singh, S. A (2014) A review on outdoor propagation models in radio communication. International Journal of Computer Engineering & Science, March 2014.
[7] Abhayawardhana, V. S., Wassell, I. J., Crosby, D., Sellars, M. P., & Brown, M. G. (2005, May). Comparison of empirical propagation pathloss models for fixed wireless access systems. In Vehicular Technology Conference, 2005. VTC 2005-Spring. 2005 IEEE 61st (Vol. 1, pp. 73-77). IEEE.
[8] Kostanic, I., Guerra, I., Faour, N., Zec, J., & Susanj, M. (2003). Optimization and Application of WCY Lee Micro-cell Propagation Model in 850MHz Frequency Band. In Proceedings of Wireless Networking Symposium.
[9] Anderson H. R., (2003) “Fixed Broadband Wireless System Design: The Creation of Global Mobile Communications”, John Wiley & Sons, Inc., New York, NY, (2003).
[10] ANDERSON, H. R. (1997). Coverage Prediction for Digital Mobile Systems Part 1 [Online]. Available from: http://mrtmag.com/mag/radio_coverage_prediction digital2/index.html [Accessed on: 22/04/2006].
[11] Boggia, G., Camarda, P. and D’Alconzo, A, (2007) Modelling of Call Dropping in Well-Established Cellular Networks. EURASIP Journal on Wireles Communications and Networking. 2007: 1-11
[12] Deme, A., Dajab, D., Buba Bajoga, M. M. A., & Choji, D. (2013). Hata-Okumura Model Computer Analysis for Path Loss Determination at 900MHz for Maiduguri, Nigeria. Mathematical Theory and Modeling, 3 (3), 1-9.
[13] Hata M., “Empirical formula for propagation loss in land mobile radio services,” IEEE Transactions on Vehicular Technology, vol. vol. VT-29, September 1981.
[14] Neskovic, A., Neskovic, N., and Paunovic, G. (2000). Modern Approaches in Modeling of Mobile Radio Systems Propagation Environment. IEEE Commun. Surveys.
[15] Okumura, Y., Ohmori, E., Kawano, T., & Fukuda, K. (1968). Field strength and its variability in VHF and UHF land-mobile radio service. Rev. Elec. Commun. Lab, 16 (9), 825-73.
[16] Ogbulezie, J. C., Onuu, M. U., Ushie, J. O., & Usibe, B. E. (2013). Propagation models for GSM 900 and 1800 MHz for Port Harcourt and Enugu, Nigeria. Network and Communication Technologies, 2 (2), 1.
[17] Obot, A., Simeon, O., & Afolayan, J. (2011). Comparative analysis of path loss prediction models for urban macrocellular environments. Nigerian journal of technology, 30 (3), 50-59.
[18] Nadir, Z., & Ahmad, M. I. (2010, March). Pathloss Determination Using Okumura-Hata Model And Cubic Regression For Missing Data For Oman. In Proceedings of the International MultiConference of Engineering and Computer scientist 2010 (Vol. 2).
[19] 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). pus, 8-9 April 2015.
Cite This Article
  • APA Style

    Steve Worgu, Samuel Godwin Ajumo, Njumoke N. Odu. (2017). Comparative Evaluation of the Pathloss Prediction Performance Hata-Okumura Pathloss Model for Urban, Suburban and Rural Areas. International Journal of Systems Science and Applied Mathematics, 2(1), 42-50. https://doi.org/10.11648/j.ijssam.20170201.16

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    ACS Style

    Steve Worgu; Samuel Godwin Ajumo; Njumoke N. Odu. Comparative Evaluation of the Pathloss Prediction Performance Hata-Okumura Pathloss Model for Urban, Suburban and Rural Areas. Int. J. Syst. Sci. Appl. Math. 2017, 2(1), 42-50. doi: 10.11648/j.ijssam.20170201.16

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    AMA Style

    Steve Worgu, Samuel Godwin Ajumo, Njumoke N. Odu. Comparative Evaluation of the Pathloss Prediction Performance Hata-Okumura Pathloss Model for Urban, Suburban and Rural Areas. Int J Syst Sci Appl Math. 2017;2(1):42-50. doi: 10.11648/j.ijssam.20170201.16

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  • @article{10.11648/j.ijssam.20170201.16,
      author = {Steve Worgu and Samuel Godwin Ajumo and Njumoke N. Odu},
      title = {Comparative Evaluation of the Pathloss Prediction Performance Hata-Okumura Pathloss Model for Urban, Suburban and Rural Areas},
      journal = {International Journal of Systems Science and Applied Mathematics},
      volume = {2},
      number = {1},
      pages = {42-50},
      doi = {10.11648/j.ijssam.20170201.16},
      url = {https://doi.org/10.11648/j.ijssam.20170201.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20170201.16},
      abstract = {In this paper, comparative evaluation of the pathloss prediction performance of the popular Hata-Okumura model for urban, suburban and rural areas is carried out. The study is based on empirical measurements conducted at Imo state university campus for 800 MHz GSM network. The prediction performance of the three categories of the Hata-Okumura model is analyzed and compared in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Prediction Accuracy (PA). The results showed that the Hata-Okumura model for the Urban area has the best prediction accuracy when not optimised but it has the lowest optimised prediction accuracy. On the other hand the Hata-Okumura model for the rural/open area has the least prediction accuracy when not optimised but it has the highest prediction accuracy when it is optimised. The results show that adoption of a model because it has the highest prediction accuracy is not the best approach to the selection of pathloss models. Rather the models should be optimised and the best optimised model should be adopted.},
     year = {2017}
    }
    

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    T1  - Comparative Evaluation of the Pathloss Prediction Performance Hata-Okumura Pathloss Model for Urban, Suburban and Rural Areas
    AU  - Steve Worgu
    AU  - Samuel Godwin Ajumo
    AU  - Njumoke N. Odu
    Y1  - 2017/02/23
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijssam.20170201.16
    DO  - 10.11648/j.ijssam.20170201.16
    T2  - International Journal of Systems Science and Applied Mathematics
    JF  - International Journal of Systems Science and Applied Mathematics
    JO  - International Journal of Systems Science and Applied Mathematics
    SP  - 42
    EP  - 50
    PB  - Science Publishing Group
    SN  - 2575-5803
    UR  - https://doi.org/10.11648/j.ijssam.20170201.16
    AB  - In this paper, comparative evaluation of the pathloss prediction performance of the popular Hata-Okumura model for urban, suburban and rural areas is carried out. The study is based on empirical measurements conducted at Imo state university campus for 800 MHz GSM network. The prediction performance of the three categories of the Hata-Okumura model is analyzed and compared in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Prediction Accuracy (PA). The results showed that the Hata-Okumura model for the Urban area has the best prediction accuracy when not optimised but it has the lowest optimised prediction accuracy. On the other hand the Hata-Okumura model for the rural/open area has the least prediction accuracy when not optimised but it has the highest prediction accuracy when it is optimised. The results show that adoption of a model because it has the highest prediction accuracy is not the best approach to the selection of pathloss models. Rather the models should be optimised and the best optimised model should be adopted.
    VL  - 2
    IS  - 1
    ER  - 

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Author Information
  • Department of Electrical/Computer Engineering, Port Harcourt Polytechnic, Rumuola, Port Harcourt, Nigeria

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

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

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