A CNN-Based Framework for Rain Attenuation Prediction in Terrestrial Millimeter-Wave Links Using Spatial Rainfall Distribution

Project Abstract

Convolutional neural network mapping regional radar reflectivity maps to signal attenuation levels across high-frequency terrestrial links.

Designed to address domain scalability challenges, this engineering prototype validates predictive modeling and computational analysis. It provides clean deployment scripts, structured models, and evaluation routines suitable for academic and industrial research labs.

Core System Features

• Optimized Modeling Pipeline: Uses state-of-the-art architectures to achieve stable convergence and high-accuracy scoring.
• Defect-Aware & Domain Adaptation Controls: Incorporates error correction routines and adaptive filtering layers to boost cross-system generalizability.
• Practical Implementation: Developed as a model for university lab cell testing and local prototyping trials.