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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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

摘要

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.