# DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization

arXiv: https://arxiv.org/abs/2407.16927 · HTML: https://mlsanity.com/paper/2407.16927/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning](https://mlsanity.com/paper/1906.08171/) (2019)
- [DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System](https://mlsanity.com/paper/2106.13632/) (2021)
- [DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor Localization](https://mlsanity.com/paper/2004.06621/) (2020)
- [The Effect of Ground Truth Accuracy on the Evaluation of Localization Systems](https://mlsanity.com/paper/2106.13614/) (2021)
- [The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phones](https://mlsanity.com/paper/2106.13663/) (2021)
- [Dejavu: An Accurate Energy-Efficient Outdoor Localization System](https://mlsanity.com/paper/1310.2342/) (2013)
- [semMatch: Road Semantics-based Accurate Map Matching for Challenging Positioning Data](https://mlsanity.com/paper/1510.03533/) (2015)
- [A Robust Zero-Calibration RF-based Localization System for Realistic Environments](https://mlsanity.com/paper/1605.02287/) (2016)

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

- [LocaGen: Low-Overhead Indoor Localization Through Spatial Augmentation](https://mlsanity.com/paper/2511.18158/) (2025)

## Abstract (arXiv)

Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.