The week's pick
Reseach Article
Prediction Characteristics of Quasi-Moment-Method Calibrated Pathloss Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 37 |
| Year of Publication: 2020 |
| Authors: Michael Adedosu Adelabu, Ayotunde Ayorinde, A. Ike Mowete |
10.5120/ijca2020920939
|
Michael Adedosu Adelabu, Ayotunde Ayorinde, A. Ike Mowete . Prediction Characteristics of Quasi-Moment-Method Calibrated Pathloss Models. International Journal of Computer Applications. 175, 37 ( Dec 2020), 21-30. DOI=10.5120/ijca2020920939
Abstract
This paper investigates the pathloss prediction characteristics of basic models, subjected to calibration with the use of a novel technique, here referred to as the Quasi-Moment-Method, QMM. After a succinct description of the QMM calibration process, the paper presents computational results involving the calibration of three different basic models-the SUI, ECC33, and Ericsson models. The results reveal that the QMM typically reduces mean prediction (MP) and root mean square (RMS) errors by several tens of decibels. One other novelty introduced by the paper, is a comparison of contributions to total predicted pathloss, by components of the basic models, and their corresponding QMM-calibrated versions.
References
- Zhang, J. J., Gentile, C. and Garey, W. 2020. On the Cross-Application of Calibrated Pathloss Models Using Area Features: Finding a way to determine similarity between areas. IEEE Antennas and Propagation Magazine, vol. 62, no. 1, Feb. 2020, pp. 40-50. DOI: https://10.1109/MAP.2019.2943272
- Benedicˇicˇ, L., Pesko, M., Javornik, T., and Korošec, P. 2014. A Metaheuristic Approach for Propagation-Model Tuning in LTE Networks. Informatica 38 (2014) Pp.135–143
- Keawbunsong, P., Duangsuwan ,S., Supanakoon, P., and Promwong, S. 2018. Quantitative Measurement of Path Loss Model Adaptation Using the Least Squares Method in an Urban DVB-T2 System International Journal of Antennas and Propagation Volume 2018, Article ID 7219618, 8 pages https://doi.org/10.1155/2018/7219618
- Popoola S.I., Atayero A.A., Faruk N., Calafate C.T., Olawoyin L.A., and Matthews V.O. 2017. Standard Propagation Model Tuning for Path Loss Predictions in Built-Up Environments. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science, VOL. 10409. Springer, Cham. https://doi.org/10.1007/978-3-319-62407-5_26
- Bolli, S. and Khan, M.Z. A. 2015. A Novel LMMSE Based Optimized Perez-Vega Zamanillo Propagation Path Loss Model in UHF/VHF Bands for India Progress In Electromagnetics Research B, Vol. 63, 2015.Pp.17–33
- Nisirat , M. A., Ismail, M Nissira , L and Al-Khawaldeh, S. 2011. A terrain roughness correction factor for Hata path loss model at 900 MHz Progress In Electromagnetics Research C, Vol. 22, 2011. Pp. 11–22
- Mawjoud, S. A., 2013 Path Loss Propagation Model Prediction for GSM Network Planning International Journal of Computer Applications (0975 – 8887) Volume 84 – No 7, December 2013 Pp 30-33 DOI: 10.5120/14592-2830
- Dahquist, G., and Bjorck, A. (Translated by Ned Anderson) 1974: Numerical Methods. Dover Publishers Inc. Mineola, New York. Chapter 4
- Harrington, R. F. 1967 "Matrix methods for field problems," in Proceedings of the IEEE, vol. 55, no. 2, pp. 136-149, Feb. 1967, DOI: https://10.1109/PROC.1967.5433.
- Adelabu, M. A. K. 2016. Optimum propagation pathloss prediction model for the Nigerian Mobile Radio Communication Environment. Ph. D. thesis, School of Postgraduate Studies, University of Lagos, Akoka, Lagos, Nigeria
Index Terms
Keywords