# Handling Device Heterogeneity for Deep Learning-based Localization

arXiv: https://arxiv.org/abs/2407.16923 · HTML: https://mlsanity.com/paper/2407.16923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 24 Jul 2024.

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

- [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)

## Abstract (arXiv)

Deep learning-based fingerprinting is one of the current promising technologies for outdoor localization in cellular networks. However, deploying such localization systems for heterogeneous phones affects their accuracy as the cellular received signal strength (RSS) readings vary for different types of phones. In this paper, we introduce a number of techniques for addressing the phones heterogeneity problem in the deep-learning based localization systems. The basic idea is either to approximate a function that maps the cellular RSS measurements between different devices or to transfer the knowledge across them. Evaluation of the proposed techniques using different Android phones on four independent testbeds shows that our techniques can improve the localization accuracy by more than 220% for the four testbeds as compared to the state-of-the-art systems. This highlights the promise of the proposed device heterogeneity handling techniques for enabling a wide deployment of deep learning-based localization systems over different devices.