基于融合数据和XGBoost-SHAP方法的5G信号的路径损失预测模型
Tingting Xu1,2, Nuo Xu1, Jay Gao3
1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
准确的5G路径损失预测对于网络规划至关重要. 这项研究使用智能手机数据和先进模型来提高5G信号预测在复杂地形的准确性.
科学领域:
- 电信工程 电信工程 电信工程
- 无线通信系统无线通信系统
- 信号传播建模 信号传播建模
背景情况:
- 准确的路径损失预测对于优化无线网络至关重要,特别是5G,它面临着复杂地形和城市环境的挑战.
- 传统模型与5G信号传播的非线性和不确定性在地形多样化和高层建筑密集的地区扎.
- 现有的方法往往缺乏有效的网络规划和在具有挑战性的环境中增强用户体验所需的精度.
研究的目的:
- 开发创新的功能工程和预测模型,用于在复杂的地形中准确的5G信号路径损失建模.
- 克服传统线性模型在预测5G信号特征方面的局限性,特别是在山区.
- 提高5G信号路径损失预测的准确性和稳定性,以改善无线网络规划.
主要方法:
- 使用智能手机作为接收器来捕获多式联网数据,包括3D结构和障碍物,用于N1和N78频段的5G信号分析.
- 实现了XGBoost算法与Optuna用于超参数调整,以优化预测模型性能.
- 采用SHAP值来解释模型的结果,并了解环境特征对信号路径损失的影响.
主要成果:
- 在5G信号路径损失预测方面取得了突破,确定系数 (R2) 为0.76和根平均平方误差 (RMSE) 为3.81dBm.
- 证明了多式联络系统和XGBoost模型在处理影响信号传播的复杂环境因素方面的有效性.
- 通过SHAP价值分析确定了影响5G信号路径损失的关键环境特征.
结论:
- 开发的模型显著提高了5G信号路径损失预测在复杂环境中的准确性和稳定性.
- 这项研究为优化无线通信网络提供了强大的技术框架和理论基础.
- 这些发现支持在具有挑战性的地形上改进未来无线通信系统的规划和部署策略.
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