  We compare the potential of neural network (NN)-based channel estimation with
classical linear minimum mean square error (LMMSE)-based estimators, also known
as Wiener filtering. For this, we propose a low-complexity recurrent neural
network (RNN)-based estimator that allows channel equalization of a sequence of
channel observations based on independent time- and frequency-domain long
short-term memory (LSTM) cells. Motivated by Vehicle-to-Everything (V2X)
applications, we simulate time- and frequency-selective channels with
orthogonal frequency division multiplex (OFDM) and extend our channel models in
such a way that a continuous degradation from line-of-sight (LoS) to
non-line-of-sight (NLoS) conditions can be emulated. It turns out that the
NN-based system cannot just compete with the LMMSE equalizer, but it also can
be trained w.r.t. resilience against system parameter mismatch. We thereby
showcase the conceptual simplicity of such a data-driven system design, as this
not only enables more robustness against, e.g., signal-to-noise-ratio (SNR) or
Doppler spread estimation mismatches, but also allows to use the same equalizer
over a wider range of input parameters without the need of re-building (or
re-estimating) the filter coefficients. Particular attention has been paid to
ensure compatibility with the existing IEEE 802.11p piloting scheme for V2X
communications. Finally, feeding the payload data symbols as additional
equalizer input unleashes further performance gains. We show significant gains
over the conventional LMMSE equalization for highly dynamic channel conditions
if such a data-augmented equalization scheme is used.
