In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available. The datasets include simulated pathloss/received signal strength (RSS) and time of arrival (ToA) radio maps over a large collection of realistic dense urban setting in real city maps. The two main applications of the presented dataset are 1) learning methods that predict the pathloss from input city maps (namely, deep learning-based simulations), and, 2) wireless localization. The fact that the RSS and ToA maps are computed by the same simulations over the same city maps allows for a fair comparison of the RSS and ToA-based localization methods.

翻译：本文介绍了一套在密集城市环境中生成的无线电地图数据集，该数据集已公开提供。这些数据集包含基于真实城市地图中大量典型密集城市场景模拟生成的路径损耗/接收信号强度（RSS）与到达时间（ToA）无线电地图。本数据集主要有两个应用方向：1）从输入城市地图预测路径损耗的学习方法（即基于深度学习的仿真方法）；2）无线定位。由于RSS与ToA地图是在相同城市地图上通过相同仿真计算获得，这为基于RSS与ToA的定位方法提供了公平的比较基准。