A method for 5G access point location determination based on PSO-BP neural network
The determination of the location of 5 G access points is of great significance for indoor positioning services and network security.This article first performs Hample filtering and dimensionality reduction on the state information of 5 G channels;then,a signal loss model based on Particle Swarm Optimization(PSO)for Back Propagation(BP)neural network was constructed,and the mapping relationship between 5 G CSI and distance was established;finally,the detection of 5 G AP is achieved based on the distance predicted by the model.The experiment used measured data from indoor detection of outdoor and indoor 5 G AP,and the results showed that compared with BP neural network,the distance prediction value based on PSO-BP neural network was more accurate.The accuracy of outdoor detection of outdoor and indoor 5 G AP reached 0.32m and 0.96m,respectively.As the number of measurement directions increases,the positioning accuracy of 5 G base stations continues to improve;when the number of directions reaches 5,the accuracy improvement is most significant.
channel state informationaccess point detectionparticle swarm optimizationback propagation neural network
万方数据
维普
