  Positioning is becoming a key component in many Internet of Things (IoT)
applications. The main challenges and limitations are the narrow bandwidth, low
power and low cost which reduces the accuracy of the time of arrival (TOA)
estimation. In this paper, we consider the positioning scenario of Narrowband
IoT (NB-IoT) that can benefit from observed time difference of arrival (OTDOA).
By applying the deep learning based technique, we explore the generalization
and feature extraction abilities of neural networks to tackle the
aforementioned challenges. As demonstrated in the numerical experiments, the
proposed algorithm can be used in different inter-site distance situations and
results in a 15% and 50% positioning accuracy improvement compared with
Gauss-Newton method in line-of-sight (LOS) scenario and non-line-of-sight
(NLOS) scenario respectively.
