A Quasi-Moment-Method-Based Calibration of Basic Pathloss Models
Keywords:pathloss model, Calibration, Moment-Method, Cross-application, ANFIS
Using a technique similar to Harringtonâ€™s method of moments, this paper develops a very simple but remarkablyÂ efficient approach to the calibration of established (basic) mobile radio propagation pathloss models. First, the theoretical foundations of the process, here referred toÂ as the â€˜Quasi-Moment-Method (QMM)â€™, is succinctly presented. Thereafter, for validation purposes, pathloss predictions due to its use are compared with corresponding data reported in the open literature, for a model that derived from the application of the Adaptive Neuro-Fuzzy Inference System, ANFIS. Results of the comparisons reveal that the root-mean square error (RMSE) values for the QMM-models compare favorably with those reported for the more computationally involved ANFIS model; and that all the six QMM-calibrated models considered in the paper, provided better spread-correlated root-mean-square (SC-RMS) and standard deviation (SD) prediction errors. QMM cross-application prediction performance is also evaluated through comparisons with measurement data obtained by the authors, for the Nigerian cities of Ibadan and Abuja. Outcomes of the comparisons clearly show that the QMM cross- application performance, particularly for the calibrated ECC-33 models, may be described as excellent.
F. Ikegami, and S. Yoshida, â€œAnalysis of multipath propagation structure in urban mobile radio environmentsâ€. IEEE Trans. on Ant. and Propagat. AP-28(4),531-537, 1980. DOI: 10.1109/TAP.1980.1142372
M. A. M. Vieira, M. E. Taylor, P. Tandon, M. Jain, R. Govidan, G. S. Sukhatme, and M. Tambe, â€œMitigating multipath fading in a mobile mesh Networkâ€, Elsevier Journal on Ad-hoc Networks, 11(4), June, 2013. pp. 1510-1521. DOI: https://doi.org/10.1016/j.adhoc.2011.01.014
T. K. Sarkar, Z. K. Kim, A. Medouri, and M. Salazar-Palma, â€œA survey of various propagation models for mobile communicationâ€. IEEE Ant and Prop Magazine. 45(3), September, 2003. pp. 51-82. DOI: 10.1109/MAP.2003.1232163
A. A. Khalek, L. Al-Kanj, Z. Dawy, and G. Turkiyah, â€œSite placement and site selection algorithms for UMTS radio planning with quality constraintsâ€. Proceedings of IEEE 17th Int Conf on Telecom, May 2010. pp. 375-381. DOI: 10.1109/ICTEL.2010.5478775
P. Calegari, F. Guidec, P. Kuonem, P. Charmaet, S. Ubeda, S. Josselin, D. Wagner, and M. Pizarosso, â€œRadio network planning with combinatorial optimisation algorithmsâ€, ACTS Mobile Telecommunications Summit 96(2), 701-713, 2010. http://www-valeria.univ ubs.fr/../..storms; accessed on 18 October 2019
K. Tutschku, Models and algorithms for demand-oriented planning of telecommunication systems, 1999. (PhD Thesis University of Wurzburg). Available from
F. Iskander, Madgy, and Zhengqing Yun, â€œPropagation Prediction Models for Wireless Communication Systemsâ€, IEEE Transactions on Microwave Theory And Techniques, VOL. 50, NO. 3, MARCH 2002. pp.662-673. DOI: 10.1109/22.989951
J. R. Fernandez, M. Quispe, G. Kemper, J. Samaniego, and D. Diaz, â€œAdjustments of log-distance path loss model for digital television in Limaâ€ XXX Simposio Brasileiro de
Telecomunicacoes, Brasilia 13-16, 2012. https://pdfs.semanticscholar.org/f892/8570f11926d0ac51b2848b0b4b7bd0684653.pdf
J. Gozalvez, M. Sepulcre, and J. A. Palazon, â€œOn the feasibility to deploy mobile industrial applications using wireless communicationsâ€ Elsevier Journal of Computers in Industry, 65(8), October, 2014. pp. 1136-1146 DOI: https://doi.org/10.1016/j.compind.2014.06.004
V. Erceg, L. J. Greenstein, S. Y. Tjandra, S. R. P. Parkoff, A. Gupta, B. Kulic, A. A. Julius, and R. Bianchi, â€œAn empirically based path loss model for wireless channels in suburban environmentsâ€ IEEE Journal on Selected Areas in Communications, 17(7), July, 1999. pp. 1205 -1211. DOI: 10.1109/49.778178
Y. Okumura, E. Ohmori, and T. Kawano, â€œField strength and its variability in VHF and UHF land mobile radio servicesâ€ !6 review of the Electrical Communications Lab., Sept.-Oct. 1968, pp. 825-873.See https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201304in1.pdf&mode=show_pdf
S. Pitchaiah, â€œRecommendations on LMDS Radio Propagation Channel Modelsâ€ IEEE 802.16.1 pc-00/44, 2000. Available from
R. Mardeni and K. F. Kwan, â€œOptimization of Hata Propagation Prediction Model In Suburban Area in Malaysiaâ€, Progress In Electromagnetics Research C, Vol. 13, pp. 91â€“106, 2010.
Liyth Nissirat, Mahamod Ismail, Mahdia Nisirat, â€œMacro-cell path loss prediction, calibration, and optimization by Leeâ€™s model for south of Amman city, Jordan at 900, and 1800 MHzâ€, Journal of Theoretical and Applied Information Technology, Vol. 41 No.2, pp. 253-258, 2012.
M. Garah, Djouane, H. Oudira, and N. Hamdiken, â€œPath Loss Models Optimization for Mobile Communication in Different Areasâ€, Indonesian Journal of Electrical Engineering and Computer Science Vol. 3, No. 1, pp. 126 - 135. July 2016. DOI: 10.11591/ijeecs.v3.i1.pp126-135 201
D. J. Y. Lee, and W. C. Y. Lee, â€œEnhanced Lee model from rough terrain sampling data aspectâ€ 2010 IEEE 72nd Vehicular Technology Conference - Fall, Ottawa, ON, 2010, pp. 1-5, doi: 10.1109/VETECF.2010.5594119.
C. Dalela, M. V. S. N. Prasad, and P. K. Dalela, â€œTuning of COST-231-Hata model for radiowave propagation predictionsâ€, David C. Wyld, et al. (Eds): CCSEA, SEA, CLOUD, DKMP, CS & IT 05, pp. 255â€“267, 2012. DOI: 10.5121/csit.2012.2227
Damosso, Eraldo, and Luis M. Correia, â€œCOST Action 231: Digital Mobile Radio Towards Future Generation Systemsâ€ Final Report European Commission, 1999. Available from https://op.europa.eu/en/publication-detail/-/publication/f2f42003-4028-4496-af95-beaa38fd475f/language-en/format-PDFA1B
Michael S. Mollel, and Michael Kisangiri, â€œComparison of Empirical Propagation Path Loss Models for Mobile Communicationâ€, Computer Engineering and Intelligent Systems, Vol.5, No.9, pp. 1-10, 2014 .
Sotirios P. Sotiroudis, Sotirios K. Goudos, Konstantinos A. Gotsis, Katherine Siakavara, and John N. Sahalos, â€œApplication of a Composite Differential Evolution Algorithm in Optimal Neural Network Design for Propagation Path-Loss Prediction in Mobile Communication Systemsâ€, IEEE Antennas and Wireless Propagation Letters, VOL. 12,, pp. 364-367, 2013. DOI: 10.1109/LAW.2015.2251994
Bruno J. Cavalcanti, Gustavo A. Cavalcante, â€œA Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHzâ€, Journal of Microwaves, Optoelectronics and Electromagnetic Applications, Vol. 16, No. 3, pp. 708-722. DOI: http://dx.doi.org/10.1590/2179-10742017v16i3925 2017
Julia O. Eichie, Onyedi D. Oyedum, Moses O. Ajewole, and Abiodun M. Aibinu â€œComparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Predictionâ€, Progress In Electromagnetics Research M, Vol. 61, 133â€“146, 2017 DOI :10.2528/PIERM17060601
S. Hosseinzadeh, H. Larijani, K. Curtis, and A. Wixted, â€œAn adaptive neuro-fuzzy propagation model for LoRaWANâ€, Applied System Innovation, vol. 2, no. 1. 2019.
Nasir Faruk, N. T. Surajudeen-Bakinde,
Abdulkarim A. Oloyede, Segun I. Popoola, A. Abdulkarim, Lukman A. Olawoyin, and Aderemi A. Atayero, â€œANFIS Model for pathloss prediction in the GSM and WCDMA bands in urban areaâ€, ELEKTRIKA, Journal of Electrical Engineering, Vol. 18(1), pp. 1-10, 2019. DOI: https://doi.org/10.11113/elektrika.v18n1.140
Nasir Faruk, Segun I. Popoola, Nazmat T. Surajudeen-Bakinde, Abdulkarim A. Oloyede, Abubakar Abdulkarim, Lukman A. Olawoyin, Maaruf Ali, Carlos T. Calafate , and Aderemi A. Atayero â€œPath Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Modelsâ€ IEEE ACCESS, Vol. 7, pp. 77293 â€“ 77307. 2019. DOI: 10.1109/ACCESS.2019.2921411
Robson D. A. Timoteo, Daniel C. Cunha and George D. C. Cavalcanti, â€œA Proposal for Path Loss Prediction in Urban Environments using Support Vector Regressionâ€, Proceedings, AICT2014: The Tenth Advanced International Conference on Telecommunications, pp. 119-124. 2014.
Xiaonan Zhao, Chunping Hou, and Qing Wang. â€œA New SVM-Based Modeling Method of Cabin Path Loss Predictionâ€, International Journal of Antennas and Propagation, Volume 2013, Article ID 279070, 7 pages http://dx.doi.org/10.1155/2013/279070.
M. Ayadi, A. Ben Zineb, and S. Tabbane, â€œA UHF Path Loss Model Using Learning Machine for Heterogeneous Networksâ€, IEEE Transactions On Antennas And Propagation, Vol.65(7). pp. 3675-3683, July 2017. DOI: 10.1109/TAP.2017.2705112
MTN (Nigeria) drive test log files, private communication.
Airtel (Nigeria) Drive Test log files, Private communication.
G. Dahlquist, and A. BjÃ¶rck, (Translated by Ned Anderson) Numerical Methods, Prentice-Hall, Inc. New Jersey. 55-110, 1974.
R. F. Harrington, â€œMatrix Methods for Field Problemsâ€, Proceedings of the IEEE, Vol. 5(2), February 1967. pp. 136-149.
Nazmat T. Surajudeen-Bakinde, Nasir Faruk, Muhammed Salman, Segun Popoola, Abdulkarim Oloyede, Lukman A. Olawoyin, â€œOn Adaptive Neuro-Fuzzy Model for Path Loss Prediction in the Vhf Bandâ€, ITU Journal: ICT Discoveries, Special Issue No. 1, Pp. 1-9, Feb, 2018.
Caleb Phillips, Douglas Sicker, and Dirk Grunwald, â€œBounding the Practical Error of Path Loss Modelsâ€, International Journal of Antennas and Propagation, Volume 2012, Article ID 754158, 21 pages. DOI:10.1155/2012/754158
Jiayi Zhang, Camillo Gentile, and Wesley Garey, â€œOn the Cross-Application of Calibrated Pathloss Models Using Area Featuresâ€, IEEE Antennas & Propagation magazine, vol. 62, no. 1, pp. 40-50, Feb. 2020. DOI: https://doi.org/10.1109/MAP.2019.2943272
How to Cite
Copyright of articles that appear in Elektrika belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.