Comparative Performance of Empirical and Machine Learning Path Loss Models for 5G Propagation in Suburban Environments of Ibadan, Nigeria

The deployment of fifth-generation (5G) mobile networks in Nigeria has intensified the need for accurate path loss prediction to support efficient network planning and coverage optimisation. However, conventional empirical propagation models, developed largely for temperate environments and lower frequency bands, remain insufficiently validated for the 3.5 GHz band in Nigerian suburban terrains, where local clutter, vegetation, and building density introduce site-specific attenuation. This study characterised 5G path loss along three suburban routes in Ibadan, Oyo State—Akinolu Town, Olodo Town, and Irisom Gas Axis—using G-Net Track Pro walk-test measurements over distances of 1–525 m at 3.5 GHz. Measured data were compared against the Free Space Model (FSM), Stanford University Interim (SUI), COST 231 Hata, and 3GPP TR 38.901 models, and further benchmarked against Random Forest and XGBoost machine learning regressors using root mean square error (RMSE). Results showed that no single empirical model performed consistently across all routes: TR 38.901 yielded the lowest RMSE at Akinolu (17.35 dB) and Irisom Gas Axis (19.37 dB), while COST 231 performed best at Olodo (14.36 dB). All empirical models exceeded the desirable 6 dB RMSE threshold, whereas Random Forest achieved RMSE values of 4.92 dB, 6.76 dB, and 4.97 dB across the three routes, consistently outperforming XGBoost and all empirical counterparts. The study concludes that data-driven models better capture complex, site-specific propagation characteristics in suburban 5G environments. It recommends that operators prioritise Random Forest and XGBoost for path loss prediction, and that the Nigerian Communications Commission encourage the development of localised 5G propagation models for spectrum planning and infrastructure deployment.