Digital Twin-Assisted High-Precision Massive MIMO Localization in Urban Canyons
Ziqin Zhou, Hui Chen, Gerhard Steinböck, Henk Wymeersch
TL;DR
This work tackles high-precision localization in urban canyons where NLOS paths and noise degrade performance. It introduces a three-stage DT-aided localization framework that combines a physics-based digital twin with RANSAC to identify LOS and single-bounce NLOS paths, treating multi-bounce paths as outliers. A probabilistic path association step generates geometric hypotheses, followed by RANSAC-based inlier selection and a final ML optimization over the inliers to estimate the UE position $oldsymbol{u}$ and clock bias $B$. Simulation results demonstrate near-oracle localization accuracy by leveraging the DT for geometry, significantly reducing deployment costs and enabling practical 6G-scale localization in challenging environments. The method shows robust path association, effective outlier rejection, and accurate parameter estimation in urban canyon scenarios.
Abstract
High-precision wireless localization in urban canyons is challenged by noisy measurements and severe non-line-of-sight (NLOS) propagation. This paper proposes a robust three-stage algorithm synergizing a digital twin (DT) model with the random sample consensus (RANSAC) algorithm to overcome these limitations. The method leverages the DT for geometric path association and employs RANSAC to identify reliable line-of-sight (LOS) and single-bounce NLOS paths while rejecting multi-bounce outliers. A final optimization on the resulting inlier set estimates the user's position and clock bias. Simulations validate that by effectively turning NLOS paths into valuable geometric information via the DT, the approach enables accurate localization, reduces reliance on direct LOS, and significantly lowers system deployment costs, making it suitable for practical deployment.