Millimeter wave (mmWave) technology is expected to be a major component of 5G wireless networks. Ultra-wide bandwidths of mmWave signals and the possibility of utilizing large number of antennas at the transmitter and the receiver allow accurate identification of multipath components in temporal and angular domains, making mmWave systems advantageous for localization applications. In this paper, we analyze the performance of a two-step mmWave localization approach that can utilize time-of-arrival, angle-of-arrival, and angle-of-departure from multiple nodes in an urban environment with both line-of-sight (LOS) and non-LOS (NLOS) links. Networks with/without radio-environmental mapping (REM) are considered, where a network with REM is able to localize nearby scatterers. Estimation of a UE location is challenging due to large numbers of local optima in the likelihood function. To address this problem, a gradient-assisted particle filter (GAPF) estimator is proposed to accurately estimate a user equipment (UE) location as well as the locations of nearby scatterers. Monte-Carlo simulations show that the GAPF estimator performance matches the Cramer-Rao bound (CRB). The estimator is also used to create a REM. It is seen that significant localization gains can be achieved by increasing beam directionality or by utilizing REM.

中文翻译：

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mmWave网络在城市环境中的无线定位。

毫米波（mmWave）技术有望成为5G无线网络的主要组成部分。mmWave信号的超宽带宽以及在发射器和接收器处使用大量天线的可能性，可以在时域和角域中准确识别多径分量，从而使mmWave系统在定位应用中具有优势。在本文中，我们分析了两步mmWave定位方法的性能，该方法可以利用城市环境中多个节点的到达时间，到达角度和离开角度，同时使用两个视线（LOS）和非视线（NLOS）链接。考虑具有/不具有无线电环境映射（REM）的网络，其中具有REM的网络能够定位附近的散射体。由于似然函数中的大量局部最优，因此估计UE位置是具有挑战性的。为了解决此问题，提出了一种梯度辅助粒子滤波器（GAPF）估计器，以准确估计用户设备（UE）的位置以及附近散射体的位置。蒙特卡洛模拟显示GAPF估计器的性能与Cramer-Rao边界（CRB）匹配。估计器还用于创建REM。可以看出，可以通过增加光束方向性或利用REM来获得显着的定位增益。蒙特卡洛模拟显示GAPF估计器的性能与Cramer-Rao边界（CRB）匹配。估计器还用于创建REM。可以看出，可以通过增加光束方向性或利用REM来获得显着的定位增益。蒙特卡洛模拟显示GAPF估计器的性能与Cramer-Rao边界（CRB）匹配。估计器还用于创建REM。可以看出，可以通过增加光束方向性或利用REM来获得显着的定位增益。