论文标题
使用无线电图实时本地化
Real-time Localization Using Radio Maps
论文作者
论文摘要
本文涉及在密集的城市场景中在蜂窝网络中的本地化问题。当设备和卫星之间没有视线时，全球导航卫星系统通常在城市环境中的性能很差，因此通常需要替代定位方法。我们提出了一种基于路径的简单而有效的定位方法。在我们的方法中，用户将是本地化报告，从一组具有已知位置的基站收到的信号强度。对于每个基站，我们在地图中每个位置的路径上都有良好的近似值，Radiounet是一个有效的基于深度学习的模拟器，对城市环境中的Pathloss函数的模拟器类似于射线追踪。使用所有基站的路径函数和报告的信号强度的近似值，我们能够提取用户位置的非常准确的近似值。
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite System typically performs poorly in urban environments when there is no line-of-sight between the devices and the satellites, and thus alternative localization methods are often required. We present a simple yet effective method for localization based on pathloss. In our approach, the user to be localized reports the received signal strength from a set of base stations with known locations. For each base station we have a good approximation of the pathloss at each location in the map, provided by RadioUNet, an efficient deep learning-based simulator of pathloss functions in urban environment, akin to ray-tracing. Using the approximations of the pathloss functions of all base stations and the reported signal strengths, we are able to extract a very accurate approximation of the location of the user.
