无人机能耗与数据传输速率部署优化OA北大核心CSTPCD

To cope with the cooperative deployment of multiple UAVs(unmanned aerial vehicles)in disaster scenarios,a genetic-quasi-Newton algorithm is proposed to optimize the energy-saving deployment and data transmission of UAVs.The characteristics of UAV channel,path loss,connectivity rate and coverage rate are extracted,and the multi-UAV cooperative deployment is abstracted as connectivity rate and coverage rate.The genetic algorithm(GA)is improved by the sorting group selection operation,the random weight crossover operation and the inconsistent mutation operator.In combination with the BFGS(Broyden-Fletcher-Goldfarb-Shanno)algorithm which improves the search direction and the iteration step length,the IGA-BFGS algorithm is proposed.This algorithm can get better optimization effect both globally and locally,and also can improve the efficiency and precision of the solution.The transmitting power and height of UAV base station are dynamically adjusted according to the channel characteristics and discrete coefficients of ground users needing rescue in different environments,which improves the power consumption efficiency of UAV base station,increases the working time of UAV base station,and realizes the energy-saving deployment of UAV base station.The simulation results show that the designed method can effectively realize the energy-saving deployment and data transmission optimization of UAV.