Research on Crosstalk Suppression of Automotive Communication System Based on Long Short-term Memory Prediction Phase Shifter

Due to the high-speed and high-frequency requirements of the communication signals in new energy vehicles, electromagnetic interference is easily generated between the cables, which will lead to the paralysis of the vehicle communication system in severe cases. To solve this problem, this paper proposes a method of inserting a phase shifter in the transmission line, and using the long short-term memory neural network to predict the position and angle of the phase shifter to suppress crosstalk. This method first uses the long short-term memory network to predict the trend of the crosstalk peak data in the transmission line, and then establishes a three-dimensional model of phase shifter parameters and suppression effects, and then selects several groups of parameters with better suppression effects by analyzing the three-dimensional model, and uses long short-term memory The network predicts the frequency of these sets of parameters to find an optimal set of parameters. Finally, use this group of parameters to connect the phase shifter to the transmission line, so as to achieve the effect of suppressing crosstalk. Research shows that the method is simple to implement and can effectively suppress crosstalk.