空对地路径损失预测的机器学习方法比较

G. Vergos, S. Sotiroudis, G. Athanasiadou, G. Tsoulos, S. Goudos
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引用次数: 1

摘要

基于机器学习的模型在路径损失预测问题上获得了越来越大的发展势头。手头的工作部署了四种机器学习算法(k最近邻- kNN,支持向量回归- SVR,随机森林- RF和AdaBoost),以模拟希腊城市Tripolis飞行基站提供的无线电覆盖。他们的比较表明,基于树的集成模型(RF和AdaBoost)可以作为射线追踪技术的快速可靠的替代方案。
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Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction
Machine Learning-based models gain increasingly momentum regarding the problem of path loss prediction. The work at hand deploys four machine learning algorithms (k Nearest Neighbors - kNN, Support Vector Regression - SVR, Random Forest - RF and AdaBoost), in order to simulate the radio coverage provided from a flying base station in the greek city of Tripolis. Their comparison shows that tree-based ensemble models (RF and AdaBoost) can be used as fast and reliable alternatives to the Ray Tracing technique.
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