Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models - Open Journal of Applied Sciences

OJAppS > Vol.12 No.6, June 2022

Volume 12, Issue 6 (June 2022)

ISSN Print: 2165-3917 ISSN Online: 2165-3925

Google-based Impact Factor: 0.92 Citations h5-index & Ranking

Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models ()

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DOI: 10.4236/ojapps.2022.126068 295 Downloads 1,809 Views Citations

Author(s)

Stephen Ojo^1*, Arif Sari², Taiwo P. Ojo³

Affiliation(s)

¹College of Engineering, Anderson University, Anderson, SC, USA.
²Department of Management Information Systems, Girne American University, Girne, Cyprus.
³Computer Science Department, Florida A&M University, Tallahassee, FL, USA.

ABSTRACT

Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.

KEYWORDS

Support Vector Regression, Radial Basis Function, Machine Learning, Path Loss, Empirical, Deterministic

Share and Cite:

Ojo, S. , Sari, A. and Ojo, T. (2022) Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models. Open Journal of Applied Sciences, 12, 990-1010. doi: 10.4236/ojapps.2022.126068.

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