# Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction

arXiv: https://arxiv.org/abs/2501.08306 · HTML: https://mlsanity.com/paper/2501.08306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [How to avoid machine learning pitfalls: a guide for academic researchers](https://mlsanity.com/paper/2108.02497/) (2021)
- [Five Disruptive Technology Directions for 5G](https://mlsanity.com/paper/1312.0229/) (2013)
- [Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines](https://mlsanity.com/paper/2306.14143/) (2023)
- [Machine Learning-Based Path Loss Modeling with Simplified Features](https://mlsanity.com/paper/2405.10006/) (2024)
- [Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.17752/) (2024)
- [A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration](https://mlsanity.com/paper/2403.14185/) (2024)

## Abstract (arXiv)

Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information systems data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and account for interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, proving model generalization through rigorous statistical assessment and the use of test set holdouts.