## 7 Conclusions  
In this paper, we provided an overview of the positioning approaches, methods, and algorithms that are envisioned and used with the help of 5G and 6G mobile networks. The main goal of the paper was to provide a comprehensive overview of Machine Learning (ML) aided positioning techniques; however, in order to introduce those, the positioning approaches used in cellular networks had to be summarized first. After describing the state-of-the-art techniques and enhanced algorithms, we surveyed the published use-case results in various application areas--especially for industrial cyber-physical systems and the V2X domain.  
Machine learning plays an important role in the control and optimization of 5G and beyond networks. Furthermore, ML solutions improve various cellular-based solutions, including positioning. Regarding 6G-supported indoor and outdoor positioning, several new approaches are being investigated, including inter-layer interactions at the Next Generation Radio Access Network (NG-RAN), as well as the extensive usage of mobile edge computing, where the location servers are closer to the NG-RAN. Although 6G standardization has just started, it is clear that there are numerous new solutions for latency control. These include allowing inter-layer interactions at the Radio Access Network, shortening the distance between the location server and the NG-RAN, and UE-based positioning. Mixing UE-based positioning with cellular-based positioning is also an interesting approach, where the UE-based calculations are supported with network-controlled measurements.  
Aside from aiming to be an extensive study of the cellular-based positioning domain, the additional value of the paper lies in its comparison tables, where the various approaches, methods, and results are compared for both the conventional solutions and those enhanced with machine learning capabilities. The subsections on lessons learned also helps the reader find the core messages and the most promising solutions for enhanced positing that are based on 5G and 6G network capabilities.  
Conceptualization, P.V. and A.P. ([PERSON]); methodology, F.M., P.R., P.V. and A.P. ([PERSON]); investigation, F.M., P.R., A.P. ([PERSON]), Z.P., I.T., P.V. and A.P. ([PERSON]); resources, P.V. and A.P.; writing--original draft preparation, F.M., P.R., A.P. ([PERSON]), Z.P., I.T., P.V. and A.P. ([PERSON]); writing--review and editing, F.M., P.V. and A.P. ([PERSON]); supervision, P.V. and A.P. ([PERSON]); funding acquisition, P.V. and A.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The research leading to these results was supported by Ericsson and the High Speed Networks Laboratory (HSNLab). Project no. 137698 has been implemented with the support provided from the National Research, Development, and Innovation Fund of Hungary, financed under the PD_21 funding scheme. This paper was supported by the [PERSON] Bolyai Research Scholarship of the Hungarian Academy of Sciences.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.