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.
关键词
信道状态信息/AP探测/粒子群优化/BP神经网络
Key words
channel state information/access point detection/particle swarm optimization/back propagation neural network
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