  IEEE 802.11p standard defines wireless technology protocols that enable
vehicular transportation and manage traffic efficiency. A major challenge in
the development of this technology is ensuring communication reliability in
highly dynamic vehicular environments, where the wireless communication
channels are doubly selective, thus making channel estimation and tracking a
relevant problem to investigate. In this paper, a novel deep learning
(DL)-based weighted interpolation estimator is proposed to accurately estimate
vehicular channels especially in high mobility scenarios. The proposed
estimator is based on modifying the pilot allocation of the IEEE 802.11p
standard so that more transmission data rates are achieved. Extensive numerical
experiments demonstrate that the developed estimator significantly outperforms
the recently proposed DL-based frame-by-frame estimators in different vehicular
scenarios, while substantially reducing the overall computational complexity.
