<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Radio communication systems are crucial for information transmission, but they face challenges like interference and fading, which significantly impact network efficiency. Understanding propagation-related issues is crucial for optimizing networks. This study examined the influence of tropospheric parameters on the Received Signal Strength (RSS) of the Globacom communication system in Rivers State. Four tropospheric variables (air temperature, relative humidity, atmospheric pressure, and wind speed) are collected from the River State University of Science and Technology (RSUST) Environmental and Climatic Observatory Program. The results of the correlation analysis indicated a direct linear correlation between air temperature and RSS. In contrast, wind speed displayed an indirect relationship, while the remaining parameters demonstrated an inverse linear relationship. Multiple linear regression (MLR) and multiple linear perceptron (MLP) models were developed to accurately predict the impacts of tropospheric parameters on RSS. A supervised-learning three-layered MLP feed-forward neural network was employed, consisting of 10 distinct MLP NN models. The Levenberg-Marquardt (LM) learning algorithm was used in training and validating the MLP NN models. The MLP 4-40-1 model showed an exceptional understanding of the non-linear relationship between the tropospheric parameters and RSS. The MLR model exhibited a weaker correlation coefficient of 0.2164, while the MLP demonstrated a stronger correlation coefficient of 0.7942. Additionally, the MLP 4-40-1 model outperformed the MLR model in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), achieving an accuracy of 0.2480 RMSE and 12.9963% MAPE, while the MLR model had 1.6880 RMSE and 27.3787% MAPE. These findings highlight the efficiency of the MLP 4-40-1 model in estimating the exact relationship between the tropospheric parameters and the signal strength of the mobile network in Rivers State. The study provides valuable insights into optimizing network design and emphasizes the significance of including tropospheric parameters in enhancing mobile communication performance.</p>
</abstract>

Radio communication systems are crucial for information transmission, but they face challenges like interference and fading, which significantly impact network efficiency. Understanding propagation-related issues is crucial for optimizing networks. This study examined the influence of tropospheric parameters on the Received Signal Strength (RSS) of the Globacom communication system in Rivers State. Four tropospheric variables (air temperature, relative humidity, atmospheric pressure, and wind speed) are collected from the River State University of Science and Technology (RSUST) Environmental and Climatic Observatory Program. The results of the correlation analysis indicated a direct linear correlation between air temperature and RSS. In contrast, wind speed displayed an indirect relationship, while the remaining parameters demonstrated an inverse linear relationship. Multiple linear regression (MLR) and multiple linear perceptron (MLP) models were developed to accurately predict the impacts of tropospheric parameters on RSS. A supervised-learning three-layered MLP feed-forward neural network was employed, consisting of 10 distinct MLP NN models. The Levenberg-Marquardt (LM) learning algorithm was used in training and validating the MLP NN models. The MLP 4-40-1 model showed an exceptional understanding of the non-linear relationship between the tropospheric parameters and RSS. The MLR model exhibited a weaker correlation coefficient of 0.2164, while the MLP demonstrated a stronger correlation coefficient of 0.7942. Additionally, the MLP 4-40-1 model outperformed the MLR model in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), achieving an accuracy of 0.2480 RMSE and 12.9963% MAPE, while the MLR model had 1.6880 RMSE and 27.3787% MAPE. These findings highlight the efficiency of the MLP 4-40-1 model in estimating the exact relationship between the tropospheric parameters and the signal strength of the mobile network in Rivers State. The study provides valuable insights into optimizing network design and emphasizes the significance of including tropospheric parameters in enhancing mobile communication performance.

Investigating the Impacts of Tropospheric Parameters on Received Signal Strength of the Mobile Communication System

Department of Electrical/Electronic Engineering

Department of Electronic & Computer Engineering

Annals of Science and Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Radio communication systems are crucial for information transmission, but they face challenges like interference and fading, which significantly impact network...

Investigating the Impacts of Tropospheric Parameters on Received Signal Strength of the Mobile Communication System

Publié en ligne: 06 janv. 2024

Pages: 63 - 73

Reçu: 26 sept. 2022

Accepté: 24 sept. 2023

DOI: https://doi.org/10.2478/ast-2023-0016

Mots clés
Artificial neural network (ANN), Meteorological parameters, Mobile communication, Multiple Linear Regression, Received signal strength (RSS)

© 2023 Bukola H. Akinwole et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Investigating the Impacts of Tropospheric Parameters on Received Signal Strength of the Mobile Communication System

Publié en ligne: 06 janv. 2024

Pages: 63 - 73

Reçu: 26 sept. 2022

Accepté: 24 sept. 2023

DOI: https://doi.org/10.2478/ast-2023-0016

Mots clésArtificial neural network (ANN), Meteorological parameters, Mobile communication, Multiple Linear Regression, Received signal strength (RSS)

© 2023 Bukola H. Akinwole et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Artificial neural network (ANN), Meteorological parameters, Mobile communication, Multiple Linear Regression, Received signal strength (RSS)