TITLE:
Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models
AUTHORS:
Stephen Ojo, Arif Sari, Taiwo P. Ojo
KEYWORDS:
Support Vector Regression, Radial Basis Function, Machine Learning, Path Loss, Empirical, Deterministic
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.12 No.6,
June
28,
2022
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.