  Data estimation is conducted with model-based estimation methods since the
beginning of digital communications. However, motivated by the growing success
of machine learning, current research focuses on replacing model-based data
estimation methods by data-driven approaches, mainly neural networks (NNs). In
this work, we particularly investigate the incorporation of existing model
knowledge into data-driven approaches, which is expected to lead to complexity
reduction and / or performance enhancement. We describe three different
options, namely "model-inspired'' pre-processing, choosing an NN architecture
motivated by the properties of the underlying communication system, and
inferring the layer structure of an NN with the help of model knowledge. Most
of the current publications on NN-based data estimation deal with general
multiple-input multiple-output communication (MIMO) systems. In this work, we
investigate NN-based data estimation for so-called unique word orthogonal
frequency division multiplexing (UW-OFDM) systems. We highlight differences
between UW-OFDM systems and general MIMO systems one has to be aware of when
using NNs for data estimation, and we introduce measures for successful
utilization of NN-based data estimators in UW-OFDM systems. Further, we
investigate the use of NNs for data estimation when channel coded data
transmission is conducted, and we present adaptions to be made, such that
NN-based data estimators provide satisfying performance for this case. We
compare the presented NNs concerning achieved bit error ratio performance and
computational complexity, we show the peculiar distributions of their data
estimates, and we also point out their downsides compared to model-based
equalizers.
