Tuning and Cross Validation of Blomquist-Ladell Model for Pathloss Prediction in the GSM 900 Mhz Frequency Band
International Journal of Theoretical and Applied Mathematics
Volume 3, Issue 2, April 2017, Pages: 94-99
Received: Oct. 25, 2016;
Accepted: Dec. 23, 2016;
Published: Apr. 18, 2017
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Njoku Chukwudi Aloziem, Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria
Ozuomba Simeon, Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria
Afolayan J. Jimoh, Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria
In this paper, Blomquist-Ladell model is tuned and used for the prediction of total pathloss for GSM signal in the 900 MHz band. The model combined free-space loss, excess plane-earth loss and tunable multiple knife-edge diffraction loss to give the total loss. Pathloss data captured from two drive test conducted in Uyo suburban area are used in the study. The first drive test dataset are used to tune the Blomquist-Ladell model, particularly to select the value of the statistical terrain diffraction loss that minimizes the root mean square error (RMSE) of the model prediction. The tuned Blomquist-Ladell model is then cross validated with the second drive test dataset. The results show that the Blomquist-Ladell model performed very well; with the training data (first drive test dataset) the model has RMSE of 2.935598 dB and Prediction Accuracy of 98.16323% and in the cross validation data (the second drive test dataset) the model has RMSE of 3.398141dB and Prediction Accuracy of 97.82251%. In both cases, the RMSE is below 3.5 dB which is below the acceptable maximum value of 7dB for such pathloss prediction model.
Njoku Chukwudi Aloziem,
Afolayan J. Jimoh,
Tuning and Cross Validation of Blomquist-Ladell Model for Pathloss Prediction in the GSM 900 Mhz Frequency Band, International Journal of Theoretical and Applied Mathematics.
Vol. 3, No. 2,
2017, pp. 94-99.
Gupta S. (2013) Comparative Path Loss Analysis Of Okumura And COST 231 Models For Wireless Mobile Communication Using MATLAB Simulation. International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 3, March – 2013.
Kumar, K. A. M. (2011). Significance of Empirical and Physical Propagation Models to Calculate the Excess Path Loss. Journal of Engineering Research and Studies, India.
Sharma, D., & Singh, R. K. (2010). The effect of path loss on Qos at NPL. International Journal of Engineering Science and Technology, 1 (2), 3018-3023.
Misra, I. S. (2013). Wireless Communications and Networks: 3G and Beyond. McGraw Hill Education (India) Pvt Ltd.
Rao, T. R., Rao, S. V. B., Prasad, M. V. S. N., Sain, M., Iqbal, A., & Lakshmi, D. R. (2000). Mobile radio propagation path loss studies at VHF/UHF bands in Southern India. IEEE transactions on Broadcasting, 46 (2), 158-164.
Rao, T. R., Rao, S. V. B., Prasad, M. V. S. N., & Sarkar, S. K. (1999). Single Knife edge diffraction propagation studies over a hilly terrain. IEEE transactions on broadcasting, 45 (1), 20-29.
Phillips, C., Raynel, S., Curtis, J., Bartels, S., Sicker, D., Grunwald, D., & McGregor, T. (2011, March). The efficacy of path loss models for fixed rural wireless links. In International Conference on Passive and Active Network Measurement (pp. 42-51). Springer Berlin Heidelberg.
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.
Hornung, R., Bernau, C., Truntzer, C., Stadler, T., & Boulesteix, A. L. (2014). Full versus incomplete cross-validation: measuring the impact of imperfect separation between training and test sets in prediction error estimation.
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79.
Cawley, G. C. (2006, July). Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 1661-1668). IEEE.
Delisle, G. Y., Lefevre, J. P., Lecours, M., & Chouinard, J. Y. (1985). Propagation loss prediction: A comparative study with application to the mobile radio channel. IEEE Transactions on Vehicular Technology, 34 (2), 86-96.
Phillips, C. T. (2012). Geostatistical techniques for practical wireless network coverage mapping.
Tocado, F. J. R., Zayas, A. D., & Gómez, P. M. (2014). Characterizing traffic performance in cellular networks. IEEE Internet Computing, 18 (1), 12-19.