# Mobile Internet Quality Estimation using Self-Tuning Kernel Regression

arXiv: https://arxiv.org/abs/2311.05641 · HTML: https://mlsanity.com/paper/2311.05641/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [A Tale of Three Datasets: Towards Characterizing Mobile Broadband Access in the United States](https://mlsanity.com/paper/2102.07288/) (2021)

## Built on by (most cited first, 2 of 2)

- [Learning Cellular Network Connection Quality with Conformal](https://mlsanity.com/paper/2407.10976/) (2024)
- [Scalable Deep Basis Kernel Gaussian Processes](https://mlsanity.com/paper/2505.18526/) (2025)

## Abstract (arXiv)

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly it imbalanced data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications.