# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 5 Conclusions  
Conditional handover is a technique used in non-terrestrial networks (NTNs) to control the handover of mobile devices between various network nodes while maintaining an uninterrupted communication session. The network technology and design of an NTN are distinct from those of a standard terrestrial network; it might make handover more difficult because a mobile device needs to hand over between various satellites and a ground station in a satellite-based NTN. We created an NTN handover simulator for this study to model and replicate the behavior of user equipment and satellite in non-terrestrial communication networks. Machine learning can improve the performance and efficiency of the handover process in wireless networks by providing more accurate and efficient handover decisions, optimizing the handover process, and mobility management. Therefore, first, we proposed a K-means algorithm with an optimal number of clusters, using the Elbow technique. After that, we built different machine learning classification models that decide or classify whether a UE is ready to hand over or not based on the distance from its cell center. The feature importance describes that features such as distance, latitude, and longitude are relevant to the model, proving why the location (UE and Satellite) triggering is very important among the conditional handover triggering defined by the 3 GPP. The 3 GPP study on NR support for non-terrestrial networks did not yet provide any dataset; therefore, we generated the dataset using the simulator that models user and satellite communication. Before applying clustering and classification algorithms, we preprocessed the data and created the distance feature using the Haversine formula. The experimental results demonstrate that the distance between a user and its cell center is a crucial factor in Handover decisions in NTN. In addition, the random forest outperformed all models with a higher accuracy of 99% and a superior F1-score of 0.9961. Conceptualization, M.K.D. ; methodology, M.K.D. and S.J. ; software, [PERSON] and S.J. ; validation, M.K.D. and S.J. ; formal analysis, M.K.D. ; investigation, M.K.D. ; resources, S.J. ; data curation, [PERSON] and M.K.D. ; writing--original draft preparation, M.K.D. ; writing--review and  
Figure 14: Loss of the neural network. Figure 13: Accuracy of the neural network. editing, [PERSON] and S.J. ; visualization, [PERSON] and S.J. ; supervision, [PERSON]; project administration, I.J. ; funding acquisition, I.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00107, Development of the technology to automate the recommendations for big data analytic models that define data characteristics and problems). The data that support the findings of this study are available on request from the corresponding author, I.J. The data are not publicly available due to their containing information that could compromise the privacy of research participants. The authors declare no conflict of interest. The data for this project are confidential but may be obtained with Data Use Agreements with the Computer Software Department of Hanyang University. Researchers interested in access to the data may contact [PERSON] at [EMAIL_ADDRESS], also see [[http://www.hanyang.ac.kr/xe/contact](http://www.hanyang.ac.kr/xe/contact)]([http://www.hanyang.ac.kr/xe/contact](http://www.hanyang.ac.kr/xe/contact)) (accessed on 6 March 2023). It can take some months to negotiate data use agreements and gain access to the data. The author will assist with any reasonable replication attempts for two years following publication. The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project 5G Fifth-generation technology standard ACK 4.1. * [PERSON] et al.