LocUNet：使用无线电地图和深度学习进行快速城市定位
LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning
摘要：
本文论述了在密集的城市场景中的蜂窝网络中的定位问题。全球导航卫星系统（GNSS）通常在城市环境中表现不佳，在城市环境中视线条件的可能性较低，因此需要替代的定位方法以获得良好的精度。我们介绍LocUNet：一种用于定位的深度学习方法，其仅基于来自基站（BS）的接收信号强度（RSS），与依赖于到达时间或到达角度信息的方法不同，其不需要在用户设备处相对于设备标准操作的计算复杂度的任何增加。在所提出的方法中，待定位的用户将来自BS的RSS报告给可以位于云中的中央处理单元（CPU）。或者，可以在用户处本地执行定位。使用BS的估计路径损耗无线电地图，LocUNet可以以最先进的精度定位用户，并且对无线电地图中的不准确性具有很高的鲁棒性。所提出的方法不需要预先采样的环境，是适合于实时应用，由于RadioUNet，一个基于神经网络的无线电地图估计。我们还介绍了两个数据集，允许在现实的城市环境中的RSS和到达时间（ToA）方法的数值比较。
英文摘要：
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions is low, and thus alternative localization methods are required for good accuracy. We present LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs), which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods that rely on time of arrival or angle of arrival information. In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit (CPU), which may be located in the cloud. Alternatively, the localization can be performed locally at the user. Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps. The proposed method does not require pre-sampling of the environment; and is suitable for real-time applications, thanks to the RadioUNet, a neural network-based radio map estimator. We also introduce two datasets that allow numerical comparisons of RSS and Time of Arrival (ToA) methods in realistic urban environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
