Digital Twin-Assisted High-Precision Massive MIMO Localization in Urban Canyons

TL;DR

This paper introduces a robust three-stage localization algorithm that combines digital twin modeling with RANSAC to improve high-precision positioning in urban canyons, effectively handling NLOS challenges and reducing deployment costs.

Contribution

It presents a novel integration of digital twin technology with RANSAC for robust urban localization, enhancing accuracy and practicality over existing methods.

Findings

Accurate localization achieved in urban canyons with NLOS conditions.

Significant reduction in system deployment costs.

Effective conversion of NLOS paths into geometric information.

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.