用地理信息特征提升无线路径损耗预测精度，验证模型泛化能力
Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction
- 引入扩展地理特征集，融合环境物理细节
- 通过留出测试集和统计检验，证明模型泛化性
- 适合无线网络规划与部署中的高精度建模需求
无线通信依赖路径损耗建模，其准确性在包含传播环境物理细节时显著提升。过去获取此类数据困难，但如今地理信息系统（GIS）数据分辨率与精度不断提高，使环境细节可得。利用这些信息，传播模型能更准确预测覆盖范围并考虑干扰问题。基于机器学习的建模方法可有效支持此任务，基于特征的方法实现高精度、高效且可扩展的传播建模。本文在前期工作基础上，引入一组扩展特征，在提升预测精度的同时，通过严格的统计评估和测试集留出，证明了模型的泛化能力。
原文摘要 · Abstract (English)
Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information systems data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and account for interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, proving model generalization through rigorous statistical assessment and the use of test set holdouts.
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