Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements. The map contains information about buildings and foliage. The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size. Further, our approach works with continuous transmitter and receiver coordinates without relying on discretization. In experiments, we show that the proposed model is able to efficiently learn dominant path losses from sparse training data and generalizes well when tested on novel maps.

翻译：估计发射机-接收机位置的路径损耗对于网络规划、切换等众多应用场景至关重要。机器学习已成为基于地图数据预测无线信道特性的常用工具。本文提出一种基于Transformer的神经网络架构，能够从不同尺寸的地图和稀疏测量数据中预测链路级特性。该地图包含建筑物和植被信息。Transformer模型会关注与路径损耗预测相关的区域，因此能够高效适配不同尺寸的地图。此外，我们的方法支持连续发射机和接收机坐标，无需依赖离散化处理。实验表明，所提模型能够从稀疏训练数据中高效学习主导路径损耗，并在测试新地图时展现出良好的泛化性能。