Car networking systems based on 5G-V2X (vehicle-to-everything) have high requirements for reliability and low-latency communication to further improve communication performance. In the V2X scenario, this article establishes an extended model (basic expansion model) suitable for high-speed mobile scenarios based on the sparsity of the channel impulse response. And propose a channel estimation algorithm based on deep learning, the method designed a multilayer convolutional neural network to complete frequency domain interpolation. A two-way control cycle gating unit (bidirectional gated recurrent unit) is designed to predict the state in the time domain. And introduce speed parameters and multipath parameters to accurately train channel data under different moving speed environments. System simulation shows that the proposed algorithm can accurately train the number of channels. Compared with the traditional car networking channel estimation algorithm, the proposed algorithm improves the accuracy of channel estimation and effectively reduces the bit error rate.
Keywords: 5G-V2X; Internet of Vehicles; base extension model; channel estimation; deep learning.