Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance
Roman Klus, Jukka Talvitie, Julia Vinogradova, Gabor Fodor, and Johan Torsner, Mikko Valkama

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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Speech and Audio Processing · Advanced Wireless Communication Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
