一种智能反射面辅助的车辆通信网络传输优化方案OA

Vehicle communication network technology can enable efficient communication and information exchange between vehicles and infrastructure,thereby improving road safety,traffic efficiency,and user experience.Given the complex propagation environment in modern cities,especially the hindrance of urban buildings and the influence of vehicle high-speed movement,the vehicle communication link is prone to degradation,resulting in impaired performance of the vehicle communication network.To obtain more reliable communication links,intelligent reflecting surfaces(IRS)have become an effective auxiliary method for vehicle communication networks.In this paper,for the vehicle-to-infrastructure communication scenario,the multi-user sum-rate optimization problem of the IRS-assisted vehicle communication network is investigated,subject to the constraint on IRS reflection coefficient and base station transmitting power.Aiming at the problem that it is difficult for the base station to obtain perfect instantaneous channel state information(CSI),an IRS-assisted optimization scheme for vehicle communication networks based on statistical CSI is designed.The optimization objective is modeled as the sum rate of multiple users.To solve the accurate acquisition of instantaneous CSI during vehicle rapid motion,a statistical CSI-based system optimization model is established.However,this optimization problem is a non-convex NP-hard problem that is difficult to solve directly.Therefore,a fractional programming method is adopted to handle the objective function.The problem is transformed into manageable subproblems by introducing two auxiliary variables.Once manageable subproblems are obtained,their mathematical expectations are calculated.Finally,an iteration algorithm based on block coordinate descent is employed to achieve joint optimization of base station beamforming and IRS passive beamforming.On the one hand,the Lagrange dual method is used to optimize the base station beamforming matrix and obtain the optimal solution.On the other hand,the sequential convex approximation method is adopted to solve the passive beamforming problem of IRS.According to the idea of the block coordinate descent algorithm,during the optimization of the base station beamforming matrix,the IRS passive beamforming is set to a fixed value,and during the optimization of the intelligent passive beamforming phase,the base station beamforming is set to a fixed value.The proposed optimization scheme is compared with two schemes:Scheme 1 is the beamforming matrix optimization scheme and the IRS random phase scheme,and Scheme 2 is the maximum ratio transmission beamforming scheme for the base station and the IRS random phase scheme.Simulation results show that compared with the benchmark schemes,the proposed scheme significantly improves the sum rate of the vehicle communication network.