American Journal of Software Engineering and Applications
Volume 6, Issue 3, June 2017, Pages: 61-66
Received: Jan. 8, 2017;
Accepted: Jan. 18, 2017;
Published: Jun. 12, 2017
Views 1722 Downloads 56
Nnadi Nathaniel Chimaobi, Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Charles Chukwuemeka Nnadi, Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Amaechi Justice Nzegwu, Department of Science Laboratory Technology Imo State Polytechnic, Umuagwo, Owerri, Nigeria
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.
Nnadi Nathaniel Chimaobi,
Charles Chukwuemeka Nnadi,
Amaechi Justice Nzegwu,
Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model, American Journal of Software Engineering and Applications.
Vol. 6, No. 3,
2017, pp. 61-66.
Sharma, P. K., & Singh, R. K. (2012). Cell coverage area and link budget calculations in GSM system. International Journal of Modern Engineering Research (IJMER) vol, 2, 170-176.
Kim, H. (2015) Wireless Communications Systems Design and Considerations. Wireless Communications Systems Design, 349-378.
Isabona, J., & Obahiagbon, K. (2014). RF Propagation Measurement and Modelling to Support Adept Planning of Outdoor Wireless Local Area Networks in 2.4 GHz Band. American Journal of Engineering Research (AJER) Volume-03, Issue-01, pp-258-267.
Hamid, N. I. B., Kawser, M. T., & Hoque, M. A. (2012). Coverage and capacity analysis of LTE radio network planning considering Dhaka city. International Journal of Computer Applications, 46 (15), 49-56.
Al Mahmud, M. R. (2009). Analysis and planning microwave link to established efficient wireless communications (Doctoral dissertation, Blekinge Institute of Technology).
Luiz, T. A., Freitas, A. R., & Guimarães, F. G. (2015, July). A New Perspective on Channel Allocation in WLAN: Considering the Total Marginal Utility of the Connections for the Users. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 879-886). ACM.
Sulyman, A. I., Nassar, A. T., Samimi, M. K., Maccartney, G. R., Rappaport, T. S., & Alsanie, A. (2014). Radio propagation path loss models for 5G cellular networks in the 28 GHz and 38 GHz millimeter-wave bands. IEEE Communications Magazine, 52 (9), 78-86.
Sotiroudis, S. P., & Siakavara, K. (2015). Mobile radio propagation path loss prediction using artificial neural networks with optimal input information for urban environments. AEU-International Journal of Electronics and Communications, 69 (10), 1453-1463.
Liechty, L. C. (2007). Path loss measurements and model analysis of a 2.4 GHz wireless network in an outdoor environment (Doctoral dissertation, Georgia Institute of Technology).
Anderson, E., Phillips, C., Sicker, D., & Grunwald, D. (2011). Modeling environmental effects on directionality in wireless networks. Mathematical and Computer Modelling, 53 (11), 2078-2092.
Gustafson, C., Abbas, T., Bolin, D., & Tufvesson, F. (2015). Statistical modeling and estimation of censored pathloss data. IEEE Wireless Communications Letters, 4 (5), 569-572.
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.
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.
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.
Akinwole, B. O. H., & Biebuma, J. J. (2013). Comparative Analysis of Empirical Path Loss Model For Cellular Transmission In Rivers State. Jurnal Ilmiah Electrical/Electronic Engineering, 2.
Costa, J. C. (2008). Analysis and optimization of empirical path Loss models and shadowing effects for the Tampa Bay Area in the 2.6 GHz Band.
Udofia, K. M., Friday, N., & Jimoh, A. J. (2016). Okumura-Hata Propagation Model Tuning Through Composite Function of Prediction Residual. Mathematical and Software Engineering, 2 (2), 93-104.
Yin, X., Cai, X., Cheng, X., Chen, J., & Tian, M. (2015). Empirical Geometry-Based Random-Cluster Model for High-Speed-Train Channels in UMTS Networks. IEEE Transactions on Intelligent Transportation Systems, 16 (5), 2850-2861.
Benedičič, L., Pesko, M., Javornik, T., & Korošec, P. (2014). A metaheuristic approach for propagation-model tuning in LTE networks. Informatica, 38 (3).
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).
Mousa, Allam, et al. "Optimizing Outdoor Propagation Model based on Measurements for Multiple RF Cell." International Journal of Computer Applications 60.5 (2012).
Udaykumar, R. Y. (2014, May). Performance investigation of mobile WiMAX protocol for aggregator and electrical vehicle communication in Vehicle-to-Grid (V2G). In Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on (pp. 1-6). IEEE.
Zhang, X., & Andrews, J. G. (2015). Downlink cellular network analysis with multi-slope path loss models. IEEE Transactions on Communications, 63 (5), 1881-1894.
Jadhav, A. N., & Kale, S. S. (2014). Suburban Area Path loss Propagation Prediction and Optimization Using Hata Model at 2375 MHz. International Journal of Advanced Research in Computer and Communication Engineering, 3 (1), 5004-5008.
Alam, D., & Khan, R. H. (2013). Comparative study of path loss models of WiMAX at 2.5 GHz frequency band. International Journal of Future Generation Communication and Networking, 6 (2), 11-24.
Imperatore, P., Salvadori, E., & Chlamtac, I. (2007, May). Path loss measurements at 3.5 GHz: a trial test WiMAX based in rural environment. In Testbeds and Research Infrastructure for the Development of Networks and Communities, 2007. TridentCom 2007. 3rd International Conference on (pp. 1-8). IEEE.
Jain, R. (2007, February). Channel models a tutorial1. In WiMAX forum AATG (pp. 1-6).