Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance

TL;DR

This paper introduces neural network-based methods utilizing novel frequency- and time-domain features for robust user equipment localization in 5G mmWave networks under NLoS conditions, demonstrating high accuracy and reliability in urban scenarios.

Contribution

It proposes new CSI feature extraction techniques and neural network models that improve localization accuracy and robustness in challenging NLoS environments, including sequence processing for tracking.

Findings

Outperforms state-of-the-art localization methods in urban NLoS scenarios.

Achieves reliable tracking of UE position, speed, and heading.

Demonstrates robustness with reduced processing complexity.

Abstract

Ensuring smooth mobility management while employing directional beamformed transmissions in 5G millimeter-wave networks calls for robust and accurate user equipment (UE) localization and tracking. In this article, we develop neural network-based positioning models with time- and frequency-domain channel state information (CSI) data in harsh non-line-of-sight (NLoS) conditions. We propose a novel frequency-domain feature extraction, which combines relative phase differences and received powers across resource blocks, and offers robust performance and reliability. Additionally, we exploit the multipath components and propose an aggregate time-domain feature combining time-of-flight, angle-of-arrival and received path-wise powers. Importantly, the temporal correlations are also harnessed in the form of sequence processing neural networks, which prove to be of particular benefit for vehicular UEs. Realistic numerical evaluations in large-scale line-of-sight (LoS)-obstructed urban environment with moving vehicles are provided, building on full ray-tracing based propagation modeling. The results show the robustness of the proposed CSI features in terms of positioning accuracy, and that the proposed models reliably localize UEs even in the absence of a LoS path, clearly outperforming the state-of-the-art with similar or even reduced processing complexity. The proposed sequence-based neural network model is capable of tracking the UE position, speed and heading simultaneously despite the strong uncertainties in the CSI measurements. Finally, it is shown that differences between the training and online inference environments can be efficiently addressed and alleviated through transfer learning.