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

Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11p Standard