Terrain- and Meteorological Influences on Path Loss for Mobile Networks in Bwari Area Council, Abuja: A Systematic Review and Meta-Analysis
Article Sidebar
Main Article Content
The design and optimization of mobile communication networks depend heavily on accurate path loss prediction, especially in settings with complicated topography and changing meteorological conditions. The predicted accuracy of traditional empirical propagation models, such Hata and COST-231, is limited in heterogeneous situations since they mainly take distance and antenna parameters into consideration, frequently ignoring the combined impact of topographical variability and weather conditions. This study presents the empirical development of a terrain–meteorological path loss model for mobile networks in Bwari Area Council, Abuja, Nigeria. By combining important climatic factors like temperature, relative humidity, and rainfall with topography descriptors like elevation, building density, and vegetation, the suggested model expands upon the traditional log-distance formulation. Multiple linear regression techniques were employed to estimate model parameters after field measurements of received signal strength were gathered in various propagation settings. Improved statistical performance indicators, such as lower root mean square error (RMSE) and higher coefficient of determination (R2), show that the addition of environmental factors considerably improves prediction accuracy when compared to traditional models. The developed model is especially well-suited for deployment in tropical and heterogeneous environments because it successfully captures both spatial and temporal fluctuations in signal transmission. By offering an experimentally verified, environment-aware approach that enhances path loss prediction and facilitates effective mobile network planning and optimization, the study advances propagation modeling. For improving coverage estimation and network performance in comparable geographic areas, the suggested model provides a scalable method.
Downloads
References
Akinbolati, A., & Abe, B. T. (2025, April). Investigating the reliability of empirical path loss models over digital terrestrial UHF channels in Ikorodu and Akure, southwestern Nigeria. In Telecom (Vol. 6, No. 2, p. 28). MDPI.
Gündüz, D., Eldar, Y. C., Goldsmith, A., & Poor, H. V. (2022). 1 Machine Learning and Communications: An Introduction. wireless communications, 1, 3.
Afape, J. O., Willoughby, A. A., Sanyaolu, M. E., Obiyemi, O. O., Moloi, K., & Dairo, O. F. (2024). Path loss modelling of mmwave outdoor propagation for 5G mobile systems at 28, 38, 60, and 73 GHz in four Nigerian cities. Discover Applied Sciences, 6(10), 495.
Chimezie, G. O., Onuchukwu, C. C., & Okereke, N. A. (2024). Determination of Pathloss Exponent 4G LTE Signal in Urban and Rural Environment of Southern Nigeria (Asaba in Delta State and Onitsha in Anambra State). Asian Basic and Applied Research Journal, 6(1), 20-35.
Akinbolati, A. M. O. A., & Ajewole, M. O. (2020). Investigation of path loss and modeling for digital terrestrial television over Nigeria. Heliyon, 6(6).
Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2017). Overview of millimeter wave communications for fifth-generation (5G) wireless networks—With a focus on propagation models. IEEE Transactions on antennas and propagation, 65(12), 6213-6230.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Wang, J., Hao, Y., & Yang, C. (2023). The current progress and future prospects of path loss model for terrestrial radio propagation. Electronics, 12(24), 4959.
Isabona, J., Imoize, A. L., Ojo, S., Lee, C. C., & Li, C. T. (2022). Atmospheric propagation modelling for terrestrial radio frequency communication links in a tropical wet and dry savanna climate. Information, 13(3), 141.
Budalal, A. A., & Islam, M. R. (2023). Path loss models for outdoor environment—with a focus on rain attenuation impact on short-range millimeter-wave links. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 3, 100106.
Galvan-Tejada, G. M., & Martinez-Enriquez, A. M. (2025). A machine learning scheme to assess the radio propagation conditions on wireless communication networks. Journal of Ambient Intelligence and Humanized Computing, 16(6), 789-802.
Moreno-Cuevas, M., Lorente-López, J., Rodríguez, J. V., Rodríguez-Rodríguez, I., & Sanchis-Borrás, C. (2025). Geospatial Feature-Based Path Loss Prediction at 1800 MHz in Covenant University Campus with Tree Ensembles, Kernel-Based Methods, and a Shallow Neural Network. Electronics, 14(20), 4112.
Umar, F. A., Abdu, A. M., Magaji, U. A., Atah, A. Y., Musa, A. G., & Lawan, S. M. (2025). IoT-Enabled Wind Energy Modelling for Irrigation in Rural and Remote Farming Communities of Babura Local Government. Indian Journal of Electrical and Electronics Engineering, 2(3), 01-07.
Onipe, J. A. (2021). Path-Loss Prediction Models for Cellular Networks in Okene, Kogi State, Nigeria (Doctoral dissertation).
Oladimeji, T. T., Kumar, P., & Oyie, N. O. (2022). Propagation path loss prediction modelling in enclosed environments for 5G networks: A review. Heliyon, 8(11).
Joseph, I., Oghu, E., & Roberts, O. O. (2023). Path loss and models: A survey and future perspective for wireless communication networks. International Journal of Advanced Networking and Applications, 15(02), 5892-5907.
Barrios-Ulloa, A., Ariza-Colpas, P. P., Sánchez-Moreno, H., Quintero-Linero, A. P., & De la Hoz-Franco, E. (2022). Modeling radio wave propagation for wireless sensor networks in vegetated environments: A systematic literature review. Sensors, 22(14), 5285.
Durgin, G. D. (2003). Space-time wireless channels. Prentice Hall Professional.
Parsons, J. D., & Parsons, P. J. D. (1992). The mobile radio propagation channel.
Saunders, S. R., & Aragón-Zavala, A. A. (2024). Antennas and propagation for wireless communication systems. John Wiley & Sons.
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.
Crane, R. K. (2003). Propagation handbook for wireless communication system design. CRC press.
Isabona, J., Imoize, A. L., Akinwumi, O. A., Omasheye, O. R., Oghu, E., Lee, C. C., & Li, C. T. (2023). Optimal Radio Propagation Modeling and Parametric Tuning Using Optimization Algorithms. Information, 14(11), 621.
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be?. IEEE Journal on selected areas in communications, 32(6), 1065-1082.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.