改进的基于极端梯度增强的算法,用于自动驾驶汽车从6 GHz以下到毫米波网络的移动性管理
Saad Ijaz Majid1, Sohaib Ijaz Majid2, Shahid Khan3
1Department of Electronics Engineering Technology, University of Technology Nowshera, Nowshera, Pakistan.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种先进的XGBoost方法,用于预测接收信号接收功率 (RSRP),以增强自动驾驶汽车的蜂交接. 该技术提高了交付成功率,确保了连接汽车的可靠连接.
科学领域:
- 电信工程 电信工程 电信工程
- 机器学习应用 机器学习应用
- 无线通信系统无线通信系统
背景情况:
- 自动驾驶汽车 (AV) 需要强大的蜂连接来确保安全运行,特别是在蜂之间交换时.
- 低接收信号接收功率 (RSRP) 可以触发过早或失败的交付,扰乱AV服务.
- 现有的交付机制可能无法充分预测高速移动的通道条件.
研究的目的:
- 开发一个准确的RSRP预测技术用于蜂交付.
- 为了提高自动驾驶汽车的连接稳定性.
- 使用预先道状态信息 (CSI) 预测来提高交付成功率 (HSR).
主要方法:
- 使用极端梯度增强 (XGBoost) 模型来预测通道状态信息 (CSI).
- 预测机制预测RSRP水平在由于信号强度低而开始交付之前.
- 该技术在各种车辆速度 (0-120公里/小时) 上进行了评估.
主要成果:
- 提出的基于XGBoost的预测技术提高了交付成功率 (HSR) 高达4%.
- 实现的平均HSR为97%,与传统方法相比为93%.
- 该解决方案在不同的频率对和无线技术中表现出有效性.
结论:
- 开发的RSRP预测技术显著提高了AVs的蜂交付可靠性.
- 基于XGBoost的CSI预测为在移动环境中保持连接提供了强大的解决方案.
- 该方法提供了一种适用于各种无线网络场景的多功能和有效方法.
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