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Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks

Saad Ijaz Majid, Sohaib Ijaz Majid, Shahid Khan, Salah Ud-Din Khan, Haider Ali, Anwar Ali Orcid Logo, Neelam Gohar, Slawomir Koziel

Scientific Reports, Volume: 15, Start page: 20870

Swansea University Author: Anwar Ali Orcid Logo

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DOI (Published version): 10.1038/s41598-025-04183-1

Abstract

This paper provides an accurate target cell’s RSRP (received signal received power) prediction technique for cellular handovers, ensuring robust connectivity for autonomous vehicles (AVs). We propose an extreme gradient boosting (XGBoost)-based mechanism to predict channel state information (CSI) in...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Nature 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69883
Abstract: This paper provides an accurate target cell’s RSRP (received signal received power) prediction technique for cellular handovers, ensuring robust connectivity for autonomous vehicles (AVs). We propose an extreme gradient boosting (XGBoost)-based mechanism to predict channel state information (CSI) in advance prior to a cell handover request due to lower RSRP. Our test results indicate that for speeds ranging from 0 to 120 km/h, the proposed prediction technique improves the handover success rate (HSR) by up to 4%. In particular, the average achieved success rate with the proposed algorithm is 97% compared to the conventional algorithm providing only 93% success rate. The proposed solution can work for any frequency pair and wireless technology.
Keywords: Channel state information (CSI); Autonomous vehicles (AVs); XGBoost; RSRP
College: Faculty of Science and Engineering
Funders: This work is partially supported by National Science Centre of Poland Grant 2020/37/B/ST7/01448 and by the Icelandic Research Fund Grant 2410297.
Start Page: 20870

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