Location Method Based On 5G OTDOA

At present,5G networks are being widely deployed,which will bring a significant improvement in cellular network performance.5G applications such as intelligent transportation systems and robots will become part of future roads,factories and society.Not only do these applications require strong communication capabilities,they also rely heavily on device location information.At present,the GNSS system widely used for positioning services has problems of low availability and severe performance degradation in urban environments,and these areas are just the focus of 5G network coverage.Positioning based on 5G signals is of great significance for achieving ubiquitous location services.The main research contents of this thesis are as follows:1.Combing the characteristics of the 5GNR downlink synchronization signal,discussing the synchronization signal processing flow and the positioning solution method based on Observed Time Difference of Arrival(OTDOA),and deducing the Cramér-Rao bound of the delay estimation.Aiming at urban macro,urban micro and indoor scenes,a 5GNR downlink synchronization signal transmission model is established,and simulation analysis is conducted.The results show that under the existing 5G deployment conditions in China,the positioning accuracy of the synchronization signal can reach 20 m and 30m(corresponding to the parameter set of 30 k Hz and 15 k Hz of the 5GNR synchronization signal,with 80% of the positioning results as a reference).If you consider the future deployment of 5G networks in higher frequency bands(the use of 120 k Hz and 240 k Hz parameter sets),the positioning accuracy can reach within 3m.2.For the scenario of 5G Ultra-dense network deployment,further study OTDOA positioning performance and optimization methods.In this thesis,based on the characteristics of Ultra-dense networks and 5G standards,the OTDOA positioning model under 5G Ultra-dense networks is established.Analysis shows that the receiving device can obtain a large amount of Time of Arrival(TOA),but OTDOA positioning accuracy is easily affected by non-line-of-sight(NLOS)signals.To solve this problem,this thesis proposes a positioning algorithm based on the combined optimization screening of Time Difference of Arrival(TDOA)to mitigate the impact of NLOS observation.The algorithm draws on the idea of clustering to screen the observations obtained by the receiving device,and then selects the best to participate in the positioning.Compared with using the binary hypothesis test decision method and the residual weighting method,it can more effectively weaken the impact of NLOS observations.Simulation results show that the algorithm can improve the positioning performance of the OTDOA positioning method in 5G Ultra-dense network scenarios.