  We consider the use of deep neural network (DNN) to develop a
decision-directed (DD)-channel estimation (CE) algorithm for multiple-input
multiple-output (MIMO)-space-time block coded systems in highly dynamic
vehicular environments. We propose the use of DNN for k-step channel prediction
for space-time block code (STBC)s, and show that deep learning (DL)-based DD-CE
can removes the need for Doppler spread estimation in fast time-varying quasi
stationary channels, where the Doppler spread varies from one packet to
another. Doppler spread estimation in this kind of vehicular channels is
remarkably challenging and requires a large number of pilots and preambles,
leading to lower power and spectral efficiency. We train two DNNs which learn
real and imaginary parts of the MIMO fading channels over a wide range of
Doppler spreads. We demonstrate that by those DNNs, DD-CE can be realized with
only rough priori knowledge about Doppler spread range. For the proposed DD-CE
algorithm, we also analytically derive the maximum likelihood (ML) decoding
algorithm for STBC transmission. The proposed DL-based DD-CE is a promising
solution for reliable communication over the vehicular MIMO fading channels
without accurate mathematical models. This is because DNN can intelligently
learn the statistics of the fading channels. Our simulation results show that
the proposed DL-based DD-CE algorithm exhibits lower propagation error compared
to existing DD-CE algorithms while the latters require perfect knowledge of the
Doppler rate.
