Performance And Trade-Off Analysis Of Radar And Communication In The Scenario Of Multi-Vehicle Collaboration

Recently,the rapid increase in the number of global vehicles has put forward higher requirements for road safety and the level of intelligent traffic services.In terms of road environment perception,automotive mmwave radar is widely used in driving assistance systems and collision avoidance systems,becoming one of the most important components of vehicular sensors.On the other hand,the vehicle-to-vehicle(V2V)communication technology in the Internet of Vehicles(IoV)can directly provide end-to-end wireless communication services for moving vehicle nodes,potentially complementing the sensing function without relying on roadside unit(RSU).Therefore,the joint automotive radar and communication(JARC)network based on the vehicle platform has become a focus of current research.To explore the impact of JARC on V2V network performance and radar detection performance,this thesis uses 1D uniform Poisson Point Process(1D-PPP)to model a single-lane,low node density scenario.The interference distribution,coverage probability,and network capacity is derived under this model.Based on this,the mean of joint detection range is calculated to measure the performance of cooperative detection.And the correctness of the theoretical analysis is verified by numerical results.This thesis then extends the model to 2D multi-lane scenario with high node density.The Matern CSMA point process is used to model the set of simultaneous active transmitting nodes.The access probability,coverage probability,and network capacity based on the integration of radar and communication are analyzed.Besides,the radar cross-section(RCS)model of vehicles is used to calculate the joint detection probability for one same target.Simulation results show that the radar detection probability is significantly improved,and the optimal performance can be obtained by setting an appropriate power allocation factor.