  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.
