Volume 5, Issue 3, May 2017, Page: 19-23
Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model
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
Received: Jan. 8, 2017;       Accepted: Jan. 24, 2017;       Published: Jun. 14, 2017
DOI: 10.11648/j.com.20170503.11      View  1700      Downloads  98
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.
Pathloss, Propagation Model, CCIR Model, Composition Function, Empirical Model, RMSE-Based Tuning Approach, Least Square Method
To cite this article
Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha, Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model, Communications. Vol. 5, No. 3, 2017, pp. 19-23. doi: 10.11648/j.com.20170503.11
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cheng, E. M., Abbas, Z., AbdulMalek, M., Lee, K. Y., You, K. Y., Khor, S. F., & Afendi, M. (2016). Geometrical Optics Based Path Loss Model for Furnished Indoor Environment. Applied Computational Electromagnetics Society Journal, 31(9), 1125-1134.
Popoola, S. I., & Oseni, O. F. (2014). Empirical Path Loss Models for GSM Network Deployment in Makurdi, Nigeria. International Refereed Journal of Engineering and Science (IRJES), 3(6), 85-94.
Chrysikos, T., & Kotsopoulos, S. (2013, March). Site-specific Validation of Path Loss Models and Large-scale Fading Characterization for a Complex Urban Propagation Topology at 2.4 GHz. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2, pp. 2078-0958).
Abdul Aziz, O., & Rahman, T. A. (2014). Investigation of Path Loss Prediction in Different Multi-Floor Stairwells at 900 MHz and 800 MHz. Progress In Electromagnetics Research M, 39, 27-39.
Chebil, J., Lawas, A. K., & Islam, M. D. (2013). Comparison between measured and predicted path loss for mobile communication in Malaysia. World Applied Sciences Journal, 21, 123-128.
Rakesh, N., & Srivatsa, S. K. (2013). A study on path loss analysis for gsm Mobile networks for urban, rural and Suburban regions of Karnataka state. International Journal of Distributed and Parallel Systems, 4(1), 53.
Faruk, N., Ayeni, A., & Adediran, Y. A. (2013). On the study of empirical path loss models for accurate prediction of TV signal for secondary users. Progress In Electromagnetics Research B, 49, 155-176.
Okorogu, V. N., Onyishi, D. U., Nwalozie, G. C., & Onoh, G. N. (2013). Empirical Characterization of Propagation Path Loss and Performance Evaluation for Co-Site Urban Environment. International Journal of Computer Applications, 70(10).
Erceg, V., Greenstein, L. J., Tjandra, S. Y., Parkoff, S. R., Gupta, A., Kulic, B.,... & Bianchi, R. (1999). An empirically based path loss model for wireless channels in suburban environments. IEEE Journal on selected areas in communications, 17(7), 1205-1211.
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.
Awada, A., Wegmann, B., Viering, I., & Klein, A. (2011). Optimizing the radio network parameters of the long term evolution system using Taguchi's method. IEEE Transactions on vehicular technology, 60(8), 3825-3839.
Phillips, C., Sicker, D., & Grunwald, D. (2013). A survey of wireless path loss prediction and coverage mapping methods. IEEE Communications Surveys & Tutorials, 15(1), 255-270.
Chebil, J., Lawas, A. K., & Islam, M. D. (2013). Comparison between measured and predicted path loss for mobile communication in Malaysia. World Applied Sciences Journal, 21, 123-128.
Sharma, P. K., & Singh, R. K. (2011). Comparative Study of Path loss Models depends on Various Parameters. IJEST, 3(6).
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).
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.
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).
Kale, S. S., & Jadhav, A. N. (2013). Performance analysis of empirical propagation models for WiMAX in urban environment. OSR J. Electron. Commun. Engin. (IOSR-JECE).
Al Mahmud, M. R. (2009). Analysis and planning microwave link to established efficient wireless communications (Doctoral dissertation, Blekinge Institute of Technology).
Walter D., (2006) R.F. Path-loss and Transmission distance calculations. Axon, LLC, Technical memorandum 2006.
Negi, A. (2006). Analysis of Relay-based Cellular Systems.
Muhammad, J. (2007). Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks.
Ajose, S. O., and Imoize, A. L. (2013). Propagation measurements and modelling at 1800 MHz in Lagos Nigeria. International Journal of Wireless and Mobile Computing, 6(2), 165-174.
Seybold, J.S. (2005) Introduction to RF Propagation, John Wiley and Sons Inc., New Jersey.
Rappaport, T.S. (2002) Wireless Communication: Principles and Practice, 2nd ed., Prentice Hall, Upper Saddle River, NJ, USA.
Browse journals by subject