  In vehicular communications, reliable channel estimation is critical for the
system performance due to the doubly-dispersive nature of vehicular channels.
IEEE 802.11p standard allocates insufficient pilots for accurate channel
tracking. Consequently, conventional IEEE 802.11p estimators suffer from a
considerable performance degradation, especially in high mobility scenarios.
Recently, deep learning (DL) techniques have been employed for IEEE 802.11p
channel estimation. Nevertheless, these methods suffer either from performance
degradation in very high mobility scenarios or from large computational
complexity. In this paper, these limitations are solved using a long short term
memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to
estimate the channel, followed by temporal averaging (TA) processing as a noise
alleviation technique. Moreover, the noise mitigation ratio is determined
analytically, thus validating the TA processing ability in improving the
overall performance. Simulation results reveal the performance superiority of
the proposed schemes compared to recently proposed DL-based estimators, while
recording a significant reduction in the computational complexity.
