TFG Final Code: Deep Learning for Air-to-Ground Propagation

This repository contains the source code and technical documentation for the neural architectures developed during this TFG (Trabajo de Fin de Grado). It includes the final proposed training/evaluation pipeline and key legacy baseline implementations that remain useful for future research.

Data files and trained weight/checkpoint artifacts are intentionally not bundled here; this repository is for code, configuration, calibration JSONs, and documentation.

Directory Structure

1. TFGEightiethTry80_preliminary_code

This is the Final Proposed Code Path (Try 80).

• Architecture: A hybrid, prior-anchored multi-task network.
• Key Innovation: Uses "frozen" physical priors (coherent two-ray path loss, regime-wise spread regressions) and learns bounded residual corrections via a shared U-Net backbone with Gaussian Mixture Model (GMM) heads.
• Purpose: Implements the final probabilistic prediction pipeline for path loss, delay spread, and angular spread.
• Contents: Training scripts (train_try80.py), evaluation logic (evaluate_try80.py), and documentation (DESIGN_TRY80.md).

2. model_pmhhnet.py

A Legacy Baseline (Try 68/PMHHNet).

• Architecture: A point-estimate residual regressor based on PMNet (Rappaport, 2023).
• Key Features: High-Frequency (HF) stem for building edge preservation and sinusoidal FiLM for UAV height conditioning.
• Purpose: While superseded by the GMM approach, it remains a lightweight and highly effective baseline for standard point-regression tasks or real-time implementations.
• Note: See the detailed description in the PMHHNet section below.

---

Technical Summary of Implementations

Feature	PMHHNet (Baseline)	Try 80 (Final)
Output Type	Point estimate (scalar)	Probabilistic (GMM parameters)
Prior Anchor	Soft / Learned	Frozen / Hard-coded
Height Conditioning	Sinusoidal FiLM	Sinusoidal FiLM + h-features
Edge Preservation	Laplacian HF Stem	Shared U-Net hierarchy
Best Use Case	Real-time / Point prediction	Full distributional analysis / High accuracy

How to use

For detailed instructions on running each implementation, please refer to the corresponding subdirectory or the technical documentation in the main thesis repository.