Received: 01 April 2026; Accepted: 06 April 2026; Published: 18 April 2026

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

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.

Keywords: Path loss, mobile network, Bwari Area Council, terrain, meteorology

significantly affects signal propagation, the applicability of traditional empirical models like Okumura–Hata and

COST-231 is frequently limited [1,2].

In Nigeria, topography features like elevation, building density, and vegetation, along with climatic elements

like temperature, humidity, and rainfall, have a significant impact on mobile network propagation. Additional

attenuation mechanisms, including diffraction, scattering, and air absorption, are introduced by these

environmental characteristics and are not sufficiently represented by conventional models. According to recent

research, prediction accuracy is greatly increased when topographical and atmospheric factors are included in

propagation models, especially in urban and suburban settings [3,4]. Furthermore, the necessity for environment-

specific propagation models that can handle high-frequency signal behavior and dynamic atmospheric effects

has increased due to the growing deployment of advanced mobile technologies, such as LTE and forthcoming

variables into path loss modeling is still inconsistent. The creation of transferable and universal propagation

models for mobile network planning in Nigeria is hampered by this fragmentation.

This article offers a comprehensive evaluation and meta-analysis of path loss models for mobile networks in

Nigeria that are reliant on geography and weather in order to close this gap. The paper evaluates model

performance using established statistical measures, summarizes previous empirical and data-driven research, and

investigates the degree to which environmental influences improve forecast accuracy. It is anticipated that the

results will offer a solid foundation for the creation of environment-specific, adaptive path loss models

appropriate for the deployment of tropical mobile networks.

In order to compile the available data on path loss models for mobile networks in Nigeria that are reliant on

receiver and remains a fundamental parameter in mobile network design. Accurate path loss prediction is

essential for coverage estimation, interference management, and network optimization. Conventional empirical

models such as Okumura–Hata, COST-231, and Egli have been widely adopted due to their simplicity and low

computational requirements. However, their predictive performance is often limited when applied to

heterogeneous environments that differ significantly from the conditions under which they were originally

developed [2,8]. The shortcomings of conventional models have become increasingly noticeable as mobile

communication technologies, especially LTE and 5G systems, have advanced. More adaptive modeling

techniques that can capture nonlinear and environment-specific propagation features are needed for higher

frequency bands and complicated propagation settings [6]. Because of this, hybrid and data-driven models that

incorporate environmental factors into path loss prediction have received more attention in recent years.

significantly increases forecast accuracy. For example, by taking into account changes in topography and

ambient clutter, terrain-adaptive modeling techniques have been demonstrated to perform better than traditional

empirical models [9]. Similarly, topography variability affects the route loss exponent and adds to seasonal

variations in signal attenuation, according to recent research conducted in Southwestern Nigeria [1]. These

results highlight how crucial terrain-aware modeling is to producing accurate propagation forecasts, especially

in areas with a variety of topographical characteristics like Nigeria.

Signal propagation is more variable due to meteorological circumstances, particularly in tropical regions like

Nigeria. Rainfall, humidity, temperature, and air pressure are important meteorological factors that impact path

loss. Through processes like absorption, scattering, and variations in air refractivity, these elements affect signal

attenuation [9]. Signal loss at higher frequencies, such as those utilized in LTE and 5G systems, has been found

Path Loss Modeling in Nigeria

Many Nigerian locations, including Abuja, Lagos, Akure, Kaduna, and Onitsha, have conducted extensive

research on path loss modeling. The majority of research concentrates on assessing and improving traditional

empirical models for regional settings. For instance, studies on mobile networks and digital terrestrial television

in Nigeria showed that standard models frequently need to be calibrated in order to provide acceptable prediction

accuracy [5]. Path loss modeling for upcoming 5G networks in Nigerian cities has also been the subject of recent

research, which has shown that topography and urban density have a major impact on signal propagation at

higher frequencies [5].

The significance of environment-specific modeling was also highlighted by research conducted in Southern

Nigeria that found differences in path loss exponent values between urban and rural settings [4]. It is challenging

Research has demonstrated that machine learning techniques can more accurately predict outcomes than

conventional models by capturing intricate nonlinear interactions between environmental factors and signal

attenuation [11,12]. Additionally, for mobile network planning, hybrid models that integrate environmental data

with physical propagation theory are becoming more and more popular. These methods have benefits, but they

also have drawbacks in terms of data accessibility, computational complexity, and model interpretability.

Path loss modeling in Nigeria has advanced significantly, however there are still a number of important gaps.

First, there is little integration of both aspects in a single modeling framework; instead, the majority of research

concentrate on either topographical or meteorological effects separately. Second, most current models are not

scalable across various environments and are site-specific.

Table 1, which highlights important factors, approaches, conclusions, constraints, and highlighted research gaps.

dominance of site-specific models and the restricted use of machine learning techniques with local datasets.

These gaps support the need for a propagation model specifically designed for the Bwari Area Council in Abuja

In mobile communication systems, accurate signal attenuation prediction depends on the use of proven

propagation models. These models serve as the basis for contemporary wireless network planning and offer

mathematical frameworks for evaluating path loss under various environmental situations. Although the

: Correction factor for the mobile antenna height, given by Equation 6.

built environments.

By extending the Hata formulation to higher frequencies (1500–2000 MHz), the COST-231 Hata model makes

it appropriate for contemporary mobile communication systems [16].

This model is particularly relevant for urban and suburban deployments in contemporary cellular networks.

Okumura Model

Based on extensive field measurements, the Okumura model is a semi-empirical method that includes correction

application across diverse environments, although it requires empirical data for accurate calibration.

Log-Normal Shadowing Model

The Log-Normal Shadowing model accounts for stochastic variations in signal strength due to environmental

obstructions such as buildings and vegetation [4].

environments where signal fluctuations are significant.

ITU-R P.1546 and P.1812 Models

The ITU-R P.1546 model incorporates environmental and terrain effects for point-to-area predictions [8]:

accounts for environmental effects such as rain and humidity and

particularly suitable for complex environments where both terrain and atmospheric conditions significantly

influence signal propagation.

Classical path loss models are widely used, although they have a number of drawbacks. The majority of models

mainly take into consideration frequency and distance, with little incorporation of environmental factors like

topography and weather. Moreover, their empirical character frequently limits its applicability to other

geographical areas. These drawbacks emphasize the necessity of adaptive and integrated models that take into

account atmospheric and topography factors, especially in tropical regions like Nigeria.

To account for environmental effects observed in field measurements, terrain and meteorological correction

terms are introduced:

Final Proposed Model

The complete terrain-meteorological path loss model is expressed as: