  Accurate indoor positioning for wireless communication systems represents an
important step towards enhanced reliability and security, which are crucial
aspects for realizing Industry 4.0. In this context, this paper presents an
investigation on the real-world indoor positioning performance that can be
obtained using a deep learning (DL)-based technique. For obtaining experimental
data, we collect power measurements associated with reference positions using a
wireless sensor network in an indoor scenario. The DL-based positioning scheme
is modeled as a supervised learning problem, where the function that describes
the relation between measured signal power values and their corresponding
transmitter coordinates is approximated. We compare the DL approach to two
different schemes with varying degrees of online computational complexity.
Namely, maximum likelihood estimation and proximity. Furthermore, we provide a
performance comparison of DL positioning trained with data generated
exclusively based on a statistical path loss model and tested with experimental
data.
