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 Wi-Fi propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity insight into multipath propagation but suffer from high computational cost and limited scalability in large 3D environments. This work investigates a hybrid approach that combines MATLAB-based ray-tracing simulations with Machine Learning to enable scalable Wi-Fi~7 network analysis. A large dataset is generated over a realistic simulated university campus, covering multiple frequency bands (2.4, 5, and 6~GHz), transmit power levels, and ray-tracing configurations with reflections and diffractions. Several regression models are evaluated, with emphasis on transformer-based architectures. Results show that the FT-Transformer accurately approximates ray-tracing outputs while reducing inference time from months to minutes. The proposed framework enables fast surrogate modeling of radio propagation and supports network planning and digital twin applications.