基于6G数字孪生的城市快速路智能信道建模OA

To support the design and development of 6th generation(6G)-enabled intelligent transportation systems,this paper investigates a digital-twin(DT)-based vehicle-to-vehicle(V2V)communication channel modeling for a representative urban expressway scenario.First,to address the complexity and diversity of traffic flow on urban expressways,traffic flow is categorized into four typical flows,including free flow,synchronized flow,congested flow,and bottleneck flow.In addition,a corresponding DT space is constructed for each traffic flow.Based on the constructed DT spaces,a DT-enabled dataset is developed to integrate V2V communication and multi-modal sensing data,which include light detection and ranging(LiDAR)point clouds,RGB images,and channel information.The dataset provides essential data support for exploring matching and mapping methods between wireless environmental information and communication channels.Second,with the assistance of LiDAR point clouds and RGB image,a systematic analysis is conducted on the scatterer parameters under the four traffic flows,which covers key characteristics,such as scatterer number,distance,angle,power,and motion velocity.Then an intelligent V2V communication channel model for urban expressway scenarios is constructed.Finally,the proposed channel model is utilized to verify channel non-stationarity and consistency,and key channel statistics are derived and simulated.Compared to ray-tracing(RT)results,the accuracy and effectiveness of the proposed urban expressway V2V channel model are verified.