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We examine the usability of deep neural networks for multiple-inputmultiple-output (MIMO) user positioning solely based on the orthogonalfrequency division multiplex (OFDM) complex channel coefficients. In contrastto other indoor positioning systems (IPSs), the proposed method does notrequire any additional piloting overhead or any other changes in thecommunications system itself as it is deployed on top of an existing OFDM MIMOsystem. Supported by actual measurements, we are mainly interested in the morechallenging non-line of sight (NLoS) scenario. However, gradient descentoptimization is known to require a large amount of data-points for training,i.e., the required database would be too large when compared to conventionalmethods. Thus, we propose a twostep training procedure, with training onsimulated line of sight (LoS) data in the first step, and finetuning onmeasured NLoS positions in the second step. This turns out to reduce therequired measured training positions and thus, reduces the effort for dataacquisition.

On Deep Learning-based Massive MIMO Indoor User Localization