Internet of vehicles has strict requirements in Ultra-Reliable and Low Latency Communications (URLLC). Especially in vehicle to infrastructure (V2I) scenario

URLLC is crucial to correctly transport and manage traffic conditions. 3GPP Cellular-V2X (C-V2X)

as the current mainstream wireless technology supporting the URLLC

still has technical challenges. In order to further improve the communication performance

this paper designs an intelligent channel estimation framework based on C-V2I specification based on the interaction between vehicle terminal

road side unit (RSU) and edge computing Internet of Vehicles server (IoV Server) in V2I communication scenario. In IoV Server

this paper proposes a channel estimation algorithm based on deep learning

which uses one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) to predict the time-domain state. By introducing additional velocity coding vector and multipath coding vector

the channel data in different mobile environments are accurately trained. Finally

system simulation and analysis show that the proposed algorithm can track the channel changes in different high-speed mobile environments through channel parameter coding

and realize the accurate training of channel data. Compared with the representative channel estimation algorithms in the IoV

the proposed algorithm improves the channel estimation accuracy

reduces the bit error rate and enhances the robustness.