DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization
Ahmed Shokry, Moustafa Youssef

TL;DR
DeepCell is a provider-side cellular localization system that uses deep learning on unlabeled cellular data to achieve high accuracy, outperforming existing client-based solutions and supporting low-end phones.
Contribution
DeepCell introduces a novel provider-side fingerprinting approach using deep neural networks trained on unlabeled cellular data for accurate localization.
Findings
Median accuracy of 29 meters in realistic environments.
Outperforms state-of-the-art client-based systems by over 75%.
Supports localization on low-end phones.
Abstract
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,…
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