Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation

Accurate channel characterization across diverse propagation environments is foundational to 5G network planning, yet existing machine learning approaches rarely integrate standardized 3GPP frameworks with vendor-specific equipment parameters. This study presents a regression-based framework combining 3GPP TR 38.901 channel models with five supervised learning algorithms—linear regression, polynomial regression (degree 2), support vector regression (SVR), decision tree, and artificial neural network (ANN)—trained on 10,000 deterministic samples spanning Urban Macro (UMa), Urban Micro (UMi), Rural Macro (RMa), and Indoor Hotspot (InH) scenarios at five carrier frequencies (0.7–60 GHz). Vendor-calibrated parameterization using authenticated Nokia AirScale 64T64R, Huawei AAU5940, and ZTE AAU 5G specifications grounds the simulated link budgets in commercial equipment characteristics, providing deployment-aligned (though formula-derived rather than field-measured) performance estimates (see Limitations). All five regression architectures are evaluated identically across all five carrier frequencies and all scenario types, enabling direct comparison under controlled conditions. For throughput prediction, the ANN and decision tree achieve the highest accuracy (R2 = 0.998, RMSE ≤ 24 Mbps averaged across five independent random splits; 95% CI: R2∈[0.997,0.999], RMSE ∈[19.1,22.4] Mbps), while linear and polynomial regressors show substantial error (R2≤0.56), reflecting the strongly nonlinear throughput surface. For path loss estimation under Urban Micro NLOS conditions, all models attain near-perfect fit (R2≈1.0, MSE 0.75 across all environments. Permutation-based feature importance identifies distance as the dominant predictor (importance 0.65–0.85), with frequency importance rising to ≈0.40 at millimeter-wave bands. Sensitivity analysis confirms robustness (R2 > 0.90) under realistic parameter perturbations (±10% distance, ±5% frequency, ±2 dB EIRP). These results provide evidence-based guidelines for model selection, training data composition, and deployment in 5G/6G network planning.