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.

翻译：无线通信依赖于路径损耗建模，当模型包含传播环境的物理细节时最为有效。获取此类数据历来具有挑战性，但地理信息系统数据正以更高的分辨率和准确性日益普及。利用这些细节信息，传播模型能够更精确地预测无线部署中的覆盖范围并解释干扰问题。基于机器学习的建模方法可显著支持这一目标，基于特征的方法能够实现准确、高效且可扩展的传播建模。在先前研究基础上，我们引入一组扩展特征集以提升预测精度，同时通过严格的统计评估与测试集保留验证，关键性地证明了模型的泛化能力。