  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.
