Wireless Localization for mmWave Networks in Urban Environments

TL;DR

This paper evaluates a two-step mmWave localization method in urban environments, utilizing multiple measurements and a novel estimator to improve accuracy, especially with radio-environmental mapping and increased beam directivity.

Contribution

It introduces a gradient-assisted particle filter estimator for mmWave localization that achieves CRB performance and leverages radio-environmental mapping for enhanced accuracy.

Findings

GAPF estimator matches the Cramer-Rao bound.

Localization improves with increased beam directivity.

Radio-environmental mapping significantly enhances localization accuracy.

Abstract

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 an REM. It is seen that significant localization gains can be achieved by increasing beam directionality or by utilizing REM.