Research On Fingerprint Localization Method Of MMIMO Terminal Based On Machine Learning

In recent years,with the rapid development of smart phones and mobile Internet services,LBS plays increasingly important role in the fields of people's lives,entertainment and security.However,under 5G technology and scenario,more challenges to the mobile terminal positioning instantaneous and accuracy have been put forward due to the complex and changeable urban environment.Fingerprint localization method is used to achieve high-precision positioning,in which the multi-path characteristics of wireless propagation could be effectively utilized,and it has a broader application prospect when combined with 5G massive MIMO technology.At present,methods and techniques on the combination of the two are under constant research.The specific research on the fingerprint localization method is carried out based on machine learning in massive MIMO single-station scenario.The main researches are as follows.(1)For the problem of poor positioning performance caused by the variability of urban environment,three kinds of fingerprints with different robustness are extracted.The first is the MACPV fingerprint extracted according to the power distribution characteristics presented by ADCPM fingerprint in angle domain,the second is the DAE fingerprint processed by denosing autoencoder based on MACPV fingerprint,and the third is the DCAE fingerprint handled by denosing convolution autoencoder based on ADCPM fingerprint.The simulation analysis using different positioning accuracy shows that as the number of scatterers decreases,the DCAE fingerprint-based positioning has better performance.(2)In view of the high cost of updating the fingerprint database in 5G network,the CAOAPFUC update algorithm is presented,which uses the method based on central AOA clustering and combines probability-based fingerprint update criterion to determine whether the fingerprint database is updated or not.The simulation results indicate that the cost of fingerprint database update can be effectively reduced with the method under the premise of the same positioning accuracy as the violent update algorithm.(3)Aiming at realizing real-time localization and simplifying the online phase process,the fast fingerprint localization method based on PQ algorithm is proposed,which improves the positioning efficiency through the codebook establishment and codeword search method based on ADCPM fingerprint.The improvement of positioning speed for the PQ algorithm is limited with the increasing size of database.Therefore,the modified CNN fast positioning method based on multitask learning is introduced which can estimate terminal position very efficiently by feedforward neural network calculation that does not depend on the size of database.The simulation results demonstrate that when the size of database is large,the advantages of the CNN-based algorithm are obvious,and the positioning speed is improved to the millisecond level under the condition that the positioning accuracy is close to the WKNN-based algorithm.