3D Urban Outdoor WiFi 7 Network Planning and Analysis Using Ray-Tracing and Machine Learning: Transformer-Based Surrogate Modeling for High-Resolution Digital Twin

Accurate modeling of outdoor wireless propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity multipath insight; however they suffer from high computational cost and limited scalability in large 3D environments. This work proposes a hybrid framework combining MATLAB-based (MATLAB 2024b 24.2.0.2773142, 64-bit, 22 October 2024) ray tracing and Machine Learning for scalable Wi-Fi 7 channel analysis. A large dataset is generated over a realistic university campus across multiple frequency bands, transmit powers, and reflection/diffraction configurations. Several regression models are evaluated, with emphasis on transformer-based architectures. The FT-Transformer achieves a Mean Absolute Error (MAE) of 3.49 dB, RMSE of 5.36 dB, and an R2 of 99.63% for validation, reducing computation time from months of simulation to seconds at inference. The framework enables accurate and efficient surrogate modeling for network planning and digital twin applications.