Intelligent Channel Estimation Based on Edge Computing for C-V2I

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. © 2021, Chinese Institute of Electronics. All right reserved.