  We showcase the practicability of an indoor positioning system (IPS) solely
based on Neural Networks (NNs) and the channel state information (CSI) of a
(Massive) multiple-input multiple-output (MIMO) communication system, i.e.,
only build on the basis of data that is already existent in today's systems. As
such our IPS system promises both, a good accuracy without the need of any
additional protocol/signaling overhead for the user localization task. In
particular, we propose a tailored NN structure with an additional phase branch
as feature extractor and (compared to previous results) a significantly reduced
amount of trainable parameters, leading to a minimization of the amount of
required training data. We provide actual measurements for indoor scenarios
with up to 64 antennas covering a large area of 80m2. In the second part,
several robustness investigations for real-measurements are conducted, i.e.,
once trained, we analyze the recall accuracy over a time-period of several
days. Further, we analyze the impact of pedestrians walking in-between the
measurements and show that finetuning and pre-training of the NN helps to
mitigate effects of hardware drifts and alterations in the propagation
environment over time. This reduces the amount of required training samples at
equal precision and, thereby, decreases the effort of the costly training data
acquisition
