Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/B5G (beyond 5G) systems. In this paper, we propose and evaluate a data-driven and model-free pathloss prediction method, dubbed PMNet. This method uses a supervised learning approach: training a neural network (NN) with a limited amount of ray tracing (or channel measurement) data and map data and then predicting the pathloss over location with no ray tracing data with a high level of accuracy. Our proposed pathloss map prediction-oriented NN architecture, which is empowered by state-of-the-art computer vision techniques, outperforms other architectures that have been previously proposed (e.g., UNet, RadioUNet) in terms of accuracy while showing generalization capability. Moreover, PMNet trained on a 4-fold smaller dataset surpasses the other baselines (trained on a 4-fold larger dataset), corroborating the potential of PMNet.

翻译：路径损耗预测是无线网络规划的重要组成部分。尽管基于射线追踪的方法已成功应用多年，但随着5G/B5G（超5G）系统中网络密度增加和/或更高频率的使用，其显著的计算开销可能变得难以承受。本文提出并评估了一种数据驱动且无模型的路径损耗预测方法，称为PMNet。该方法采用监督学习策略：利用有限量的射线追踪（或信道测量）数据与地图数据训练神经网络（NN），随后在无射线追踪数据的位置上以高精度预测路径损耗。我们提出的面向路径损耗地图预测的神经网络架构，借助最先进的计算机视觉技术进行增强，在准确性上优于此前提出的其他架构（如UNet、RadioUNet），同时展现出泛化能力。此外，使用4倍更小数据集训练的PMNet超越了使用4倍更大数据集训练的其他基线方法，进一步证实了PMNet的潜力。