A Novel Propagation Pathloss Model Calibration Tool

Ayotunde Abimbola Ayorinde; Hisham Abubakar Muhammed; Francis Olutunji Okewole; Ike Mowete

doi:10.15866/irecap.v11i3.19796

A Novel Propagation Pathloss Model Calibration Tool

Ayotunde Abimbola Ayorinde⁽¹⁾, Hisham Abubakar Muhammed⁽²⁾, Francis Olutunji Okewole⁽³⁾, Ike Mowete^(4*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecap.v11i3.19796

Abstract

This paper develops and comprehensively evaluates a very simple and remarkably efficient method, here referred to as the Quasi-Moment-Method (QMM), as a tool for the calibration of basic (classical) pathloss models, in various radiowave propagation scenarios. After a succinct description of the characterizing features of the method, the paper presents computational results involving comparisons with published data concerning Minimum Mean Square Error (MMSE) optimization, Adaptive Neuro-Fuzzy Inference System (ANFIS) pathloss modeling, and (for an indoor case), a dual-slope reference model. The results reveal that the QMM remarkably outperforms the MMSE-optimized and modified single-slope, close-in reference models, when evaluated in terms of the statistical measures of Root Mean Square Error (RMSE), Mean Prediction Error (MPE), and Standard Deviation Error (SDE). Computational results due to the QMM also compare favorably (and better, with some performance metrics) with published corresponding results available from the literature, in which heuristic (ANFIS and Artificial Neural Network (ANN)) as well as geospatial (Kriging) models were utilized. A number of inherent properties of the real, symmetric ‘model calibration matrices’ associated with QMM process are identified in the paper, as offering interesting possibilities for further investigations involving eigenvalues and corresponding eigenvectors.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords

Moment Method; Propagation Pathloss; Model Calibration; Empirical; Indoor and Outdoor Cases

Full Text:

PDF

References

Tapan K. Sarkar, Zhong Ji, Kyungjung Kim, Abdellatif Medour, Magdalena Salazar-Palma, A survey of various propagation models for mobile communications, IEEE Antennas and Propagation Magazine, Volume 45, (Issue 3), 2003, Pages 51-82.
https://doi.org/10.1109/map.2003.1232163

Agbotiname L. Imoize, Abiodun I. Dosunmu, Path Loss Characterization of Long Term Evolution Network for Lagos, Nigeria, Jordan Journal of Electrical Engineering, Volume 4, (Issue 2), 2018, Pages 114-128.

G. R. Mardeni, K. F. Kwan, Optimization of Hata Propagation Prediction Model In Suburban Area in Malaysia, Progress In Electromagnetics Research C, Volume 13, 2010, Pages 91–106.
https://doi.org/10.2528/pierc10011804

Nasir Faruk, Segun I. Popoola, Nazmat T. Surajudeen-Bakinde, Abdulkarim A. Oloyede, Abubakar Abdulkarim, Lukman A. Olawoyin, Maaruf Ali, Carlos T. Calafate, Aderemi A. Atayero, Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models, IEEE ACCESS, Volume 7, 2019, Pages 77293 – 77307.
https://doi.org/10.1109/access.2019.2921411

Julia O. Eichie, Onyedi D. Oyedum, Moses O. Ajewole, Abiodun M. Aibinu, Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction, Progress In Electromagnetics Research M, Volume 61, 2017, pages 133–146.
https://doi.org/10.2528/pierm17060601

Nasir Faruk, N. T. Surajudeen-Bakinde, Abdulkarim A. Oloyede, Segun I. Popoola, A. Abdulkarim, Lukman A. Olawoyin, Aderemi A. Atayero, ANFIS Model for pathloss prediction in the GSM and WCDMA bands in urban area, ELEKTRIKA, Journal of Electrical Engineering, Volume 18, (Issue 1), 2019, Pages 1-10.
https://doi.org/10.11113/elektrika.v18n1.140

Segun I. Popoola, Aderemi A. Atayero, Oluwafunso A. Popoola, Comparative assessment of data obtained using empirical models for path loss predictions in a university campus environment, Data in Brief 18, 2018, Pages 380–393.
https://doi.org/10.1016/j.dib.2018.03.040

Michael S. Mollel, Michael Kisangiri, Comparison of Empirical Propagation Path Loss Models for Mobile Communication, Computer Engineering and Intelligent Systems, Volume 5, (Issue 9), 2014, Pages 1-10.

Purnima K. Sharma, R. K. Singh, Comparative Analysis of Propagation Path loss Models with Field Measured Data, International Journal of Engineering Science and Technology, Volume 2, (Issue 6), 2010, Pages 2008-2013.

M Garah, Djouane, H Oudira, N Hamdiken, Path Loss Models Optimization for Mobile Communication in Different Areas, Indonesian Journal of Electrical Engineering and Computer Science, Volume 3, (Issue 1), July 2016, Pages 126-135.
https://doi.org/10.11591/ijeecs.v3.i1.pp126-135

Liyth Nissirat, Mahamod Ismail, Mahdia Nisirat, Macro-cell path loss prediction, calibration, and optimization by lee’s model for south of Amman city, Jordan at 900, and 1800 MHz, Journal of Theoretical and Applied Information Technology, Volume 41, (Issue 2), 2012, Pages 253-258.
https://doi.org/10.1109/rfm.2011.6168779

Robson D. A. Timoteo, Daniel C. Cunha, George D. C. Cavalcanti, A Proposal for Path Loss Prediction in Urban Environments using Support Vector Regression, Proceedings, AICT2014: The Tenth Advanced International Conference on Telecommunications, pp. 119-124, 2014.

M. Ayadi, A. Ben Zineb, S. Tabbane, A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks, IEEE Trans. Antennas Propag., Volume 65, (Issue 7), July 2017, Pages 3675-3683.
https://doi.org/10.1109/tap.2017.2705112

S. Hosseinzadeh, H. Larijani, K. Curtis, A. Wixted, An adaptive neuro-fuzzy propagation model for LoRaWAN, Applied System Innovation, Volume 2, (Issue 1), 2019.
https://doi.org/10.3390/asi2010010

Sotirios P. Sotiroudis, Sotirios K. Goudos, Konstantinos A. Gotsis, Katherine Siakavara, John N. Sahalos, Application of a Composite Differential Evolution Algorithm in Optimal Neural Network Design for Propagation Path-Loss Prediction in Mobile Communication Systems, IEEE Antennas and Wireless Propag. Letters, Volume 12, 2013. Pp. 364-367.
https://doi.org/10.1109/lawp.2013.2251994

Ruichen Wang , Jingyang Lu , Yiran Xu , Dan Shen , Genshe Chen ,Khanh Pham , Erik Blasch, Intelligent Path Loss Prediction Engine Design using Machine Learning in the Urban Outdoor Environment, Proceedings of the SPIE on sensors and systems for space applications, Volume 10641, Pages 106410j-1 - 106410j-7.
https://doi.org/10.1117/12.2305204

Bruno J. Cavalcanti, Gustavo A. Cavalcante, Laércio M. de Mendonça, Gabriel M. Cantanhede, Marcelo M.M.de Oliveira, Adaildo G. D’Assunção, A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz, Journal of Microwaves, Optoelectronics and Electromagnetic Applications, Volume 16, (Issue 3), 2017, Pages 708-722.
https://doi.org/10.1590/2179-10742017v16i3925

A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri, Semi Deterministic Hybrid model for Path Loss prediction improvement, Procedia Computer Science, Volume 92, 2016, Pages 336 – 344.
https://doi.org/10.1016/j.procs.2016.07.388

Zyad Nossire, Navarun Gupta, Laiali Almazaydeh, Xingguo Xiong, New Empirical Path Loss Model for 28 GHz and 38 GHz Millimeter Wave in Indoor Urban under Various Conditions, Appl. Sci., Volume 8, 2018, Pages 1-14.
https://doi.org/10.3390/app8112122

Nicholas O. Oyie, Thomas J. O. Afullo, Measurements and Analysis of Large-Scale Path Loss Model at 14 and 22 GHz in Indoor Corridor, IEEE Access, Volume 6, (Special section on modeling, analysis, and design of 5g ultra-dense networks), 2018, Pages 17205-17214.
https://doi.org/10.1109/access.2018.2802038

A. Zyoud, J. Chebil, M. H. Habaebi, M. R. Islam, A. K. Lwas, Investigation of Three Dimensional Empirical Indoor Path Loss Models for Femtocell Networks, Proceedings, 5th International Conference on Mechatronics (ICOM’13), Volume 53, 2013.
https://doi.org/10.1088/1757-899x/53/1/012021

Dalton Czane Gomes Valadares, Joseana Macdo Fechine Rgis de Arajo, Marco Aurlio Spohn, Angelo Perkusich, Kyller Costa Gorgnio, Elmar Uwe, Kurt Melcher, 802.11g: Signal Strength Evaluation in an Industrial Environment, Internet of Things, Volume 9, 2020, Pages 1-10.

Di Wu, Gang Zhu, Bo Ai, Application of Artificial Neural Networks for Path Loss Prediction in Railway Environments, Proceedings 4th International ICST Workshop on Channel Measurement Modeling, 2011.
https://doi.org/10.4108/iwoncmm.2010.10

N. Sabri, S. S. Mohammed, Sarah Fouad, A. A. Syed, Fahad Taha AL-Dhief, Auda Raheemah, Investigation of Empirical Wave Propagation Models in Precision Agriculture, MATEC Web of Conferences, Vol. 150, 06020, pp. 1-5, 2018.
https://doi.org/10.1051/matecconf/201815006020

G. Dahlquist, A. Björck, (Translated by Ned Anderson), Numerical Methods (Dover Publications Inc. Mineola, New York, 1974, Section 4.2, pp. 88-92).

Roger F. Harrington, Matrix Methods for Field Problems, Proceedings of the IEEE, Volume 55, (Issue 2), 1967, Pages 136-149.

Guan-Yi Liu, Tsung-Yu Chang, Yung-Chun Chiang, Po-Chiang Lin, Jeich Mar, Path Loss Measurements of Indoor LTE System for the Internet of Things, Appl. Sc., Volume 7, (Issue 537), 2017, Pages 1-8.
https://doi.org/10.3390/app7060537

Ebhota, V., Srivastava, V., Modeling Environmental Effects on Electromagnetic Signal Propagation Using Multi-Layer Perceptron Artificial Neural Network, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (3), pp. 175-182.
https://doi.org/10.15866/irecap.v10i3.18135

G. Nazmat, T. Surajudeen-Bakinde, Nasir Faruk, Muhammed Salman, Segun Popoola, Abdulkarim Oloyede, Lukman A. Olawoyin, On Adaptive Neuro-Fuzzy Model for Path Loss Prediction in the Vhf Band, ITU Journal: ICT Discoveries, (Special Issue 1), February 2018, Pages 1-9.
https://doi.org/10.1109/iccni.2017.8123768

Caleb Phillips, Douglas Sicker, Dirk Grunwald, Bounding the Practical “Error of Path Loss Models”, International Journal of Antennas and Propagation Volume 2012, 2012. Article ID 754158, 21 pages.
https://doi.org/10.1155/2012/754158

Katruksa, S., Jiriwibhakorn, S., Evaluation of Mid-Term Load Forecasting Case Study Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs), (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 283-293.
https://doi.org/10.15866/iree.v15i4.17766

Qasim, M., Velkin, V., Maximum Power Point Tracking Techniques for Micro-Grid Hybrid Wind and Solar Energy Systems - a Review, (2020) International Journal on Energy Conversion (IRECON), 8 (6), pp. 223-234.
https://doi.org/10.15866/irecon.v8i6.19502

Shatnawi, M., Bani Yassein, M., Aljawarneh, S., Alodibat, S., Meqdadi, O., Hmeidi, I., Al Zoubi, O., An Improvement of Neural Network Algorithm for Anomaly Intrusion Detection System, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (2), pp. 84-93.
https://doi.org/10.15866/irecap.v10i2.18735

Refbacks

There are currently no refbacks.

Username
Password
Remember me