# Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2411.17752 · HTML: https://mlsanity.com/paper/2411.17752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [How to avoid machine learning pitfalls: a guide for academic researchers](https://mlsanity.com/paper/2108.02497/) (2021)
- [Machine Learning-Based Path Loss Modeling with Simplified Features](https://mlsanity.com/paper/2405.10006/) (2024)
- [Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements](https://mlsanity.com/paper/2202.05107/) (2022)

## Built on by (most cited first, 3 of 3)

- [Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction](https://mlsanity.com/paper/2501.08306/) (2025)
- [Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction](https://mlsanity.com/paper/2504.03625/) (2025)
- [PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting](https://mlsanity.com/paper/2605.08035/) (2026)

## Abstract (arXiv)

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.