# Uncertainty Estimation for Path Loss and Radio Metric Models

arXiv: https://arxiv.org/abs/2501.06308 · HTML: https://mlsanity.com/paper/2501.06308/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [Guaranteed Coverage Prediction Intervals with Gaussian Process Regression](https://mlsanity.com/paper/2310.15641/) (2023)
- [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)

## Built on by (most cited first, 1 of 1)

- [Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty](https://mlsanity.com/paper/2511.23243/) (2025)

## Abstract (arXiv)

This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based radio metric models [1] as well as in a 2-D map-based ML path loss model [2]. Utilizing diverse difficulty estimators, we construct 95% confidence prediction intervals (PIs) that are statistically robust. Our experiments demonstrate that CPS models, trained on Toronto datasets, generalize effectively to other cities such as Vancouver and Montreal, maintaining high coverage and reliability. Furthermore, the employed difficulty estimators identify challenging samples, leading to measurable reductions in RMSE as dataset difficulty decreases. These findings highlight the effectiveness of scalable and reliable uncertainty estimation through CPS in wireless network modeling, offering important potential insights for network planning, operations, and spectrum management.