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.

翻译：本文提出了一种物理信息机器学习方法用于路径损耗预测。该方法通过在训练阶段同时纳入（i）空间损耗场之间的物理依赖关系与（ii）该场中的实测路径损耗值来实现。研究表明，所提出的学习问题的解能在使用少量神经网络层和参数的情况下提升泛化能力与预测质量。后者可实现快速推理时间，这对定位等下游任务尤为有利。此外，物理信息公式允许使用少量训练数据进行训练与预测，使其适用于广泛的实际路径损耗预测场景。