Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks

Oladayo Gbolahan Ajileye; Vincent Andrew Akpan; Joseph Sunday Ojo

doi:doi:10.11648/j.wcmc.20261301.11

Research Article |

| Peer-Reviewed

Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks

Oladayo Gbolahan Ajileye

, Vincent Andrew Akpan^*

, Joseph Sunday Ojo

Published in International Journal of Wireless Communications and Mobile Computing (Volume 13, Issue 1)

Received: 7 August 2026 Accepted: 20 August 2026 Published: 22 September 2026

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Abstract

The convergence of terrestrial wireless and satellite communication systems within Satellite–Terrestrial Integrated Networks (STINs) for sixth-generation (6G) communications has increased the need for accurate and adaptive link performance prediction. Conventional analytical and empirical propagation models often fail to capture the nonlinear effects of atmospheric impairments, particularly rain attenuation in tropical regions. This study evaluates two supervised machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for predicting the signal-to-noise ratio (SNR), a key wireless communication link budget parameter. Rain rate, rain attenuation, and link budget were used as input variables with propagation datasets collected from ten climatically diverse locations across Nigeria. The datasets were partitioned into training, validation, and testing sets using a 70:15:15 ratio, while Bayesian optimization determined the optimal hyper-parameters of both models. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R²), regression analysis, and actual-versus-predicted time-series comparisons. Results show that both models accurately captured the nonlinear relationship between propagation parameters and SNR across all study locations. However, SVM consistently outperformed RF, recording lower RMSE, MAE, and MSE values in nine of the ten locations and achieving R² values between 0.99 and 1.00. SVM also tracked severe fading events caused by intense rain attenuation more accurately, whereas RF exhibited larger deviations during deep fades. Minna was the only location where RF produced lower prediction errors. These findings demonstrate that SVM provides superior prediction accuracy and generalization for SNR estimation under tropical propagation conditions, making it a reliable tool for wireless link budget prediction and future 6G satellite–terrestrial network planning.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 13, Issue 1)
DOI	10.11648/j.wcmc.20261301.11
Page(s)	1-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Satellite–Terrestrial Integrated Networks, Wireless Communication, Link Budget, Signal-to-Noise Ratio Optimization, Support Vector Machine, Random Forest, Rain Attenuation, Sixth Generation (6G) Networks

References

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Ajileye, O. G., Akpan, V. A., Ojo, J. S. (2026). Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks. International Journal of Wireless Communications and Mobile Computing, 13(1), 1-22. https://doi.org/10.11648/j.wcmc.20261301.11

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ACS Style

Ajileye, O. G.; Akpan, V. A.; Ojo, J. S. Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks. Int. J. Wirel. Commun. Mobile Comput. 2026, 13(1), 1-22. doi: 10.11648/j.wcmc.20261301.11

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AMA Style

Ajileye OG, Akpan VA, Ojo JS. Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks. Int J Wirel Commun Mobile Comput. 2026;13(1):1-22. doi: 10.11648/j.wcmc.20261301.11

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@article{10.11648/j.wcmc.20261301.11,
  author = {Oladayo Gbolahan Ajileye and Vincent Andrew Akpan and Joseph Sunday Ojo},
  title = {Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {13},
  number = {1},
  pages = {1-22},
  doi = {10.11648/j.wcmc.20261301.11},
  url = {https://doi.org/10.11648/j.wcmc.20261301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20261301.11},
  abstract = {The convergence of terrestrial wireless and satellite communication systems within Satellite–Terrestrial Integrated Networks (STINs) for sixth-generation (6G) communications has increased the need for accurate and adaptive link performance prediction. Conventional analytical and empirical propagation models often fail to capture the nonlinear effects of atmospheric impairments, particularly rain attenuation in tropical regions. This study evaluates two supervised machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for predicting the signal-to-noise ratio (SNR), a key wireless communication link budget parameter. Rain rate, rain attenuation, and link budget were used as input variables with propagation datasets collected from ten climatically diverse locations across Nigeria. The datasets were partitioned into training, validation, and testing sets using a 70:15:15 ratio, while Bayesian optimization determined the optimal hyper-parameters of both models. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R2), regression analysis, and actual-versus-predicted time-series comparisons. Results show that both models accurately captured the nonlinear relationship between propagation parameters and SNR across all study locations. However, SVM consistently outperformed RF, recording lower RMSE, MAE, and MSE values in nine of the ten locations and achieving R2 values between 0.99 and 1.00. SVM also tracked severe fading events caused by intense rain attenuation more accurately, whereas RF exhibited larger deviations during deep fades. Minna was the only location where RF produced lower prediction errors. These findings demonstrate that SVM provides superior prediction accuracy and generalization for SNR estimation under tropical propagation conditions, making it a reliable tool for wireless link budget prediction and future 6G satellite–terrestrial network planning.},
 year = {2026}
}

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TY  - JOUR
T1  - Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks
AU  - Oladayo Gbolahan Ajileye
AU  - Vincent Andrew Akpan
AU  - Joseph Sunday Ojo
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.wcmc.20261301.11
DO  - 10.11648/j.wcmc.20261301.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
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PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.wcmc.20261301.11
AB  - The convergence of terrestrial wireless and satellite communication systems within Satellite–Terrestrial Integrated Networks (STINs) for sixth-generation (6G) communications has increased the need for accurate and adaptive link performance prediction. Conventional analytical and empirical propagation models often fail to capture the nonlinear effects of atmospheric impairments, particularly rain attenuation in tropical regions. This study evaluates two supervised machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for predicting the signal-to-noise ratio (SNR), a key wireless communication link budget parameter. Rain rate, rain attenuation, and link budget were used as input variables with propagation datasets collected from ten climatically diverse locations across Nigeria. The datasets were partitioned into training, validation, and testing sets using a 70:15:15 ratio, while Bayesian optimization determined the optimal hyper-parameters of both models. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R2), regression analysis, and actual-versus-predicted time-series comparisons. Results show that both models accurately captured the nonlinear relationship between propagation parameters and SNR across all study locations. However, SVM consistently outperformed RF, recording lower RMSE, MAE, and MSE values in nine of the ten locations and achieving R2 values between 0.99 and 1.00. SVM also tracked severe fading events caused by intense rain attenuation more accurately, whereas RF exhibited larger deviations during deep fades. Minna was the only location where RF produced lower prediction errors. These findings demonstrate that SVM provides superior prediction accuracy and generalization for SNR estimation under tropical propagation conditions, making it a reliable tool for wireless link budget prediction and future 6G satellite–terrestrial network planning.
VL  - 13
IS  - 1
ER  -

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Oladayo Gbolahan Ajileye

Department of Physics, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0009-0003-8036-2449
Vincent Andrew Akpan

Department of Biomedical Engineering, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0000-0002-5155-2863
Joseph Sunday Ojo

Department of Physics, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0000-0001-9329-1799

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Ajileye, O. G., Akpan, V. A., Ojo, J. S. (2026). Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks. International Journal of Wireless Communications and Mobile Computing, 13(1), 1-22. https://doi.org/10.11648/j.wcmc.20261301.11

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Ajileye, O. G.; Akpan, V. A.; Ojo, J. S. Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks. Int. J. Wirel. Commun. Mobile Comput. 2026, 13(1), 1-22. doi: 10.11648/j.wcmc.20261301.11

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AMA Style

Ajileye OG, Akpan VA, Ojo JS. Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks. Int J Wirel Commun Mobile Comput. 2026;13(1):1-22. doi: 10.11648/j.wcmc.20261301.11

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@article{10.11648/j.wcmc.20261301.11,
  author = {Oladayo Gbolahan Ajileye and Vincent Andrew Akpan and Joseph Sunday Ojo},
  title = {Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {13},
  number = {1},
  pages = {1-22},
  doi = {10.11648/j.wcmc.20261301.11},
  url = {https://doi.org/10.11648/j.wcmc.20261301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20261301.11},
  abstract = {The convergence of terrestrial wireless and satellite communication systems within Satellite–Terrestrial Integrated Networks (STINs) for sixth-generation (6G) communications has increased the need for accurate and adaptive link performance prediction. Conventional analytical and empirical propagation models often fail to capture the nonlinear effects of atmospheric impairments, particularly rain attenuation in tropical regions. This study evaluates two supervised machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for predicting the signal-to-noise ratio (SNR), a key wireless communication link budget parameter. Rain rate, rain attenuation, and link budget were used as input variables with propagation datasets collected from ten climatically diverse locations across Nigeria. The datasets were partitioned into training, validation, and testing sets using a 70:15:15 ratio, while Bayesian optimization determined the optimal hyper-parameters of both models. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R2), regression analysis, and actual-versus-predicted time-series comparisons. Results show that both models accurately captured the nonlinear relationship between propagation parameters and SNR across all study locations. However, SVM consistently outperformed RF, recording lower RMSE, MAE, and MSE values in nine of the ten locations and achieving R2 values between 0.99 and 1.00. SVM also tracked severe fading events caused by intense rain attenuation more accurately, whereas RF exhibited larger deviations during deep fades. Minna was the only location where RF produced lower prediction errors. These findings demonstrate that SVM provides superior prediction accuracy and generalization for SNR estimation under tropical propagation conditions, making it a reliable tool for wireless link budget prediction and future 6G satellite–terrestrial network planning.},
 year = {2026}
}

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TY  - JOUR
T1  - Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks
AU  - Oladayo Gbolahan Ajileye
AU  - Vincent Andrew Akpan
AU  - Joseph Sunday Ojo
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.wcmc.20261301.11
DO  - 10.11648/j.wcmc.20261301.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 1
EP  - 22
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20261301.11
AB  - The convergence of terrestrial wireless and satellite communication systems within Satellite–Terrestrial Integrated Networks (STINs) for sixth-generation (6G) communications has increased the need for accurate and adaptive link performance prediction. Conventional analytical and empirical propagation models often fail to capture the nonlinear effects of atmospheric impairments, particularly rain attenuation in tropical regions. This study evaluates two supervised machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for predicting the signal-to-noise ratio (SNR), a key wireless communication link budget parameter. Rain rate, rain attenuation, and link budget were used as input variables with propagation datasets collected from ten climatically diverse locations across Nigeria. The datasets were partitioned into training, validation, and testing sets using a 70:15:15 ratio, while Bayesian optimization determined the optimal hyper-parameters of both models. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R2), regression analysis, and actual-versus-predicted time-series comparisons. Results show that both models accurately captured the nonlinear relationship between propagation parameters and SNR across all study locations. However, SVM consistently outperformed RF, recording lower RMSE, MAE, and MSE values in nine of the ten locations and achieving R2 values between 0.99 and 1.00. SVM also tracked severe fading events caused by intense rain attenuation more accurately, whereas RF exhibited larger deviations during deep fades. Minna was the only location where RF produced lower prediction errors. These findings demonstrate that SVM provides superior prediction accuracy and generalization for SNR estimation under tropical propagation conditions, making it a reliable tool for wireless link budget prediction and future 6G satellite–terrestrial network planning.
VL  - 13
IS  - 1
ER  -

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