英文文献：Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques-使用学习机器和降维技术预测城市环境中的路径损失
英文文献作者：Mauro Piacentini,Francesco Rinaldi
英文文献摘要：
Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.

在现代移动通信系统中，路径损耗预测是网络规划的一项重要工作。基于学习机器的模型似乎是预测传播路径损失的经验和确定性方法的有效替代。由于学习机的性能取决于输入特征的数量，一个获得更可靠模型的好方法是使用降低数据维数的技术。本文提出了一种将学习机与降维技术相结合的新方法。我们报告了一个真实数据集的结果，显示了基于学习机器的方法的效率和降维技术在提高预测精度方面的有用性。