论文标题
用于Pathloss预测的物理信息神经网络
Physics-informed neural networks for pathloss prediction
论文作者
论文摘要
本文介绍了一种针对Pathloss预测的物理知识的机器学习方法。这是通过同时在训练阶段（i）空间损耗场和（ii）测量的田间途径值之间的物理依赖性来实现的。结果表明，提出的学习问题的解决方案通过少量的神经网络层和参数改善了概括和预测质量。后者会导致快速推理时间，这有利于诸如本地化之类的下游任务。此外，物理知识的配方允许使用少量培训数据进行培训和预测，从而使其吸引了广泛的实用途径预测场景。
This paper introduces a physics-informed machine learning approach for pathloss prediction. This is achieved by including in the training phase simultaneously (i) physical dependencies between spatial loss field and (ii) measured pathloss values in the field. It is shown that the solution to a proposed learning problem improves generalization and prediction quality with a small number of neural network layers and parameters. The latter leads to fast inference times which are favorable for downstream tasks such as localization. Moreover, the physics-informed formulation allows training and prediction with a small amount of training data which makes it appealing for a wide range of practical pathloss prediction scenarios.
