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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
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last_indexed 2025-07-04T06:42:55Z
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spelling 2025-07-03T14:11:29.1339044 v2 69883 2025-07-03 Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks f206105e1de57bebba0fd04fe9870779 0000-0001-7366-9002 Anwar Ali Anwar Ali true false 2025-07-03 ACEM 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. Journal Article Scientific Reports 15 20870 Springer Nature 2045-2322 Channel state information (CSI); Autonomous vehicles (AVs); XGBoost; RSRP 1 7 2025 2025-07-01 10.1038/s41598-025-04183-1 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee 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. 2025-07-03T14:11:29.1339044 2025-07-03T14:00:29.5367654 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Saad Ijaz Majid 1 Sohaib Ijaz Majid 2 Shahid Khan 3 Salah Ud-Din Khan 4 Haider Ali 5 Anwar Ali 0000-0001-7366-9002 6 Neelam Gohar 7 Slawomir Koziel 8 69883__34663__662c5e6f6bc240c58a35fd6783ff4118.pdf 41598_2025_Article_4183.pdf 2025-07-03T14:00:29.5362162 Output 4064715 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
spellingShingle Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
Anwar Ali
title_short Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
title_full Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
title_fullStr Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
title_full_unstemmed Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
title_sort Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks
author_id_str_mv f206105e1de57bebba0fd04fe9870779
author_id_fullname_str_mv f206105e1de57bebba0fd04fe9870779_***_Anwar Ali
author Anwar Ali
author2 Saad Ijaz Majid
Sohaib Ijaz Majid
Shahid Khan
Salah Ud-Din Khan
Haider Ali
Anwar Ali
Neelam Gohar
Slawomir Koziel
format Journal article
container_title Scientific Reports
container_volume 15
container_start_page 20870
publishDate 2025
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-025-04183-1
publisher Springer Nature
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
document_store_str 1
active_str 0
description 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.
published_date 2025-07-01T05:29:21Z
_version_ 1851097939271745536
score 11.089572

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