  Channel estimation forms one of the central component in current OFDM systems
that aims to eliminate the inter-symbol interference by calculating the CSI
using the pilot symbols and interpolating them across the entire time-frequency
grid. It is also one of the most researched field in the PHY with LS and MMSE
being the two most used methods. In this work, we investigate the performance
of deep neural network architecture based on CNN for channel estimation in
vehicular environments used in 3GPP Rel.14 CV2X technology. To this end, we
compare the performance of the proposed DL architectures to the legacy LS
channel estimation currently employed in C-V2X. Initial investigations prove
that the proposed DL architecture outperform the legacy CV2X channel estimation
methods especially at high mobile speeds
