# Machine Learning-Based Path Loss Modeling with Simplified Features

arXiv: https://arxiv.org/abs/2405.10006 · HTML: https://mlsanity.com/paper/2405.10006/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 16 May 2024.

## Built on by (most cited first, 9 of 9)

- [Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.17752/) (2024)
- [Target Strangeness: A Novel Conformal Prediction Difficulty Estimator](https://mlsanity.com/paper/2410.19077/) (2024)
- [Machine Learning for Modeling Wireless Radio Metrics with Crowdsourced Data and Local Environment Features](https://mlsanity.com/paper/2501.01344/) (2025)
- [Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks](https://mlsanity.com/paper/2501.07534/) (2025)
- [Automated Modeling Method for Pathloss Model Discovery](https://mlsanity.com/paper/2505.23383/) (2025)
- [Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction](https://mlsanity.com/paper/2501.08306/) (2025)
- [Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty](https://mlsanity.com/paper/2511.23243/) (2025)
- [PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting](https://mlsanity.com/paper/2605.08035/) (2026)
- [Low-Altitude Channel Multipath Prediction via Panoramic Perception and Vision-Language Model](https://mlsanity.com/paper/2607.21953/) (2026)

## Abstract (arXiv)

Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation, providing a practical solution for efficient and effective wireless network planning.