A Learning Approach for User Localization and Movement Prediction with Limited Information

Quang-Vinh Tran, Quang-Diep Pham

2025

Abstract

In the 5G network system, users continuously travel among areas managed by different User Plane Functions (UPFs), leading to the need for efficient handover between UPFs. Conventional handover relies on signal measurements between user devices and neighboring base stations, so it is a ”re-active” scheme. Therefore, this procedure results in long response time of the Packet Data Unit (PDU) session establishment, and affecting data service quality. Another approach is an ”pro-active” scheme, in which the position of users are estimated, hence the decision of UPF handover can be made earlier. We propose a solution using machine learning techniques to model user movement behavior in the network and predict user positions in advance. The predicted UPF managing the next location will be announced accordingly to take preparatory steps for serving the incoming users, thereby reducing the new PDU session establishment latency, increasing processing speed, and improving the quality of experience. We propose the model combining the K-means clustering algorithm and the Gated Recurrent Unit deep learning network for time series data. The solution was tested with Viettel’s 5G network data and demonstrated its feasibility in real-world dataset.

Paper Citation

in Harvard Style

Tran Q. and Pham Q. (2025). A Learning Approach for User Localization and Movement Prediction with Limited Information. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 717-723. DOI: 10.5220/0013221600003929

in Bibtex Style

@conference{iceis25,
author={Quang-Vinh Tran and Quang-Diep Pham},
title={A Learning Approach for User Localization and Movement Prediction with Limited Information},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={717-723},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013221600003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Learning Approach for User Localization and Movement Prediction with Limited Information
SN - 978-989-758-749-8
AU - Tran Q.
AU - Pham Q.
PY - 2025
SP - 717
EP - 723
DO - 10.5220/0013221600003929
PB - SciTePress