FR3 Path Loss in Outdoor Corridors: Physics-Guided Two-Ray Residual Learning

Jorge Celades-Martínez (), Jorge Rojas-Vivanco, Melissa Diago-Mosquera, Alvaro Peña and Jose García ()
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Jorge Celades-Martínez: Doctorado en Industria Inteligente, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Jorge Rojas-Vivanco: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Melissa Diago-Mosquera: Departamento de Electrónica, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
Alvaro Peña: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Jose García: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile

Mathematics, 2025, vol. 13, issue 17, 1-26

Abstract: Accurate path-loss characterization in the upper mid-band is critical for 5G/6G outdoor planning, yet classical deterministic expressions lose fidelity at 18 GHz, and purely data-driven regressors offer limited physical insight. We present a physics-guided residual learner that couples a calibrated two-ray model with an XGBoost regressor trained on the deterministic residuals. To enlarge the feature space without promoting overfitting, synthetic samples obtained by perturbing antenna height and ground permittivity within realistic bounds are introduced with a weight of w = 0.3 . The methodology is validated with narrowband measurements collected along two straight 25 m corridors. Under cross-corridor transfer, the hybrid predictor attains 0.59 – 0.62 dB RMSE and R 2 ≥ 0.996 , reducing the error of a pure-ML baseline by half and surpassing deterministic formulas by a factor of four. Small-scale analysis yields decorrelation lengths of 0.23 m and 0.41 m; a cross-correlation peak of unity at Δ = 0.10 m confirms the physical coherence of both corridors. We achieve <1 dB error using a small set of field measurements plus simple synthetic data. The method keeps a clear mathematical core and can be extended to other priors, NLOS cases, and semi-open hotspots.

Keywords: upper mid-band; FR3; path-loss modeling; physics-informed machine learning; two-ray model; residual learning; outdoor corridors; 18 GHz; synthetic data augmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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