  We examine the usability of deep neural networks for multiple-input
multiple-output (MIMO) user positioning solely based on the orthogonal
frequency division multiplex (OFDM) complex channel coefficients. In contrast
to other indoor positioning systems (IPSs), the proposed method does not
require any additional piloting overhead or any other changes in the
communications system itself as it is deployed on top of an existing OFDM MIMO
system. Supported by actual measurements, we are mainly interested in the more
challenging non-line of sight (NLoS) scenario. However, gradient descent
optimization is known to require a large amount of data-points for training,
i.e., the required database would be too large when compared to conventional
methods. Thus, we propose a twostep training procedure, with training on
simulated line of sight (LoS) data in the first step, and finetuning on
measured NLoS positions in the second step. This turns out to reduce the
required measured training positions and thus, reduces the effort for data
acquisition.
