Research for Road Scenario-Oriented V2V Small-Scale Channel Model Parameter Extraction and Optimization

V2X communication is crucial for intelligent connected vehicles, but suffers from multipath fading. In existing studies, V2X channel modeling primarily employs 2D models or follows the models specified in 3GPP TR36.885. The reliability has not been verified, and it cannot reflect the small-scale fading of multipath fading. There are also problems, such as easy local optimality and slow convergence in model parameter optimization. Therefore, based on the V2V 3D channel model, this paper uses the K-Means++ algorithm to obtain the main category data, takes the main category data as the input, and then uses the genetic algorithm (GA) to perform multi-parameter optimization of the reflection point M and reflection coefficient μ of six scenarios to obtain the optimal parameters. Based on the China Communications Standards Association (CCSA)’s white paper, the Rice factor K was determined. In-chamber and on-road comparison tests were designed to verify the rationality of the parameters optimized by the GA. The experiments show that this model and method can accurately reproduce the characteristics of V2V channels, support the setting of indoor V2X test parameters, and provide a standardized solution for the verification of V2V communication performance.