# 5G Positioning: An Analysis of Early Datasets
## 5 Conclusions  
This experiment provided the opportunity to accomplish the following aims:  
* to implement algorithms for positioning using 5G observations that were applied to 5G data from the GINTOSG experiment;
* to experimentally assess the accuracy of the positioning in an environment where the deployment of the base stations was carefully controlled and optimized;
* to conduct experimental research on techniques aimed at identifying and reducing measurement errors.  
The processing focused on \(TDoA\) observations, processed in a single epoch using the least squares method. The accuracies of the estimated positions reach the decimeter and the meter levels, respectively, in outdoor and indoor scenarios. Within this specific experiment, the SNRs of the base stations do not exhibit a significant correlation with distance with respect to the receiver; moreover, the use of the SNR as a criterion to choose the reference station in \(TDoA\) does not improve the quality of the differenced observations. This can be explained by the fact that the GINTOSG experimental area was relatively small, spanning 40 m \(\times\) 40 m; moreover, the data of the experiment were calibrated in order to remove the clock biases of the base stations, and this explains the good quality of the results. In our scenario, the redundancies of the single-epoch solutions are small. Some outliers exist when obstructions and reflecting surfaces are present, especially indoors. Clearly, with larger redundancies in single epochs, or by time-series filtering, the estimates could improve. In any case, the obtained results in LOS have accuracies that range from decimeters to meters.  
Given these experimental premises, the achieved results are satisfying and promising for further research about mass-market applications. In conclusion, the hybridization of 5G and GNSS signals for positioning holds great promise for a wide range of applications, from autonomous vehicles and augmented reality to smart cities and beyond.  
Figure 17: Take 05, reference station 4: least squares results.  
Figure 18: Pie charts representing the error magnitudes in Takes 04 and 05.  
Author Contributions:Conceptualization, project administration, funding acquisition, F.C.G.; methodology, software, investigation, writing, C.P., F.C.G. and L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We express our gratitude to the Navigation Directorate of the European Space Agency (ESA) for providing hosting and research opportunities to the lead author at ESTEC, The Netherlands, from March 2022 to August 2022, enabling the study on mobile network positioning with 5G signals. We would also like to acknowledge the dedicated project team responsible for the implementation of ESA's GINTO5G project, funded under the EGEP programme, contract No 400012349718/NL/AS. It is worth noting that neither the lead author nor Politecnico di Milano were directly involved in the ESA project. Finally, the authors would like to thank the anonymous reviewers for their valuable suggestions that have significantly contributed to improving the quality of the article.  
The authors declare no conflict of interest.