  This paper studies the performance of a user positioning system using Channel
State Information (CSI) of a Massive MIMO (MaMIMO) system. To infer the
position of the user from the CSI, a Convolutional Neural Network is designed
and evaluated through a novel dataset. This dataset contains indoor MaMIMO CSI
measurements using three different antenna topologies, covering a 2.5 m by 2.5
m indoor area. We show that we can train a Convolutional Neural Network (CNN)
model to estimate the position of a user inside this area with a mean error of
less than half a wavelength. Moreover, once the model is trained on a given
scenario and antenna topology, Transfer Learning is used to repurpose the
acquired knowledge towards another scenario with significantly different
antenna topology and configuration. Our results show that it is possible to
further train the CNN using only a small amount of extra labelled samples for
the new topology. This transfer learning approach is able to reach accurate
results, paving the road to a practical CSI-based positioning system powered by
CNNs.
