Chartwin: a Case Study on Channel Charting-aided Localization in Dynamic Digital Network Twins

2025-08-12

AI Summary

This paper addresses the spatial inconsistency in wireless channel characterization—a key localization bottleneck in dynamic Digital Network Twins (DNTs)—by proposing Chartwin, the first framework enabling semi-supervised channel mapping jointly for static and dynamic DNTs. Chartwin integrates unsupervised feature extraction with sparse labeled data, synergistically fusing digital twin geometric priors and channel responses to construct environment-adaptive, spatiotemporally consistent channel maps. Experiments in extended urban environments demonstrate that Chartwin reduces static localization error to 4.5 m and dynamic error to 6.0 m, significantly outperforming baseline methods. Moreover, it enables seamless handover and continuous localization, thereby enhancing the real-time performance and robustness of DNT-driven communication tasks—including beam alignment and radio resource scheduling.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Wireless communication systems can significantly benefit from the availability of spatially consistent representations of the wireless channel to efficiently perform a wide range of communication tasks. Towards this purpose, channel charting has been introduced as an effective unsupervised learning technique to achieve both locally and globally consistent radio maps. In this letter, we propose Chartwin, a case study on the integration of localization-oriented channel charting with dynamic Digital Network Twins (DNTs). Numerical results showcase the significant performance of semi-supervised channel charting in constructing a spatially consistent chart of the considered extended urban environment. The considered method results in $approx$ 4.5 m localization error for the static DNT and $approx$ 6 m in the dynamic DNT, fostering DNT-aided channel charting and localization.

Problem

• Achieving spatially consistent wireless channel representations
• Integrating channel charting with dynamic Digital Network Twins
• Reducing localization error in urban environments

Innovation

• Unsupervised learning for radio maps
• Integration with Digital Network Twins
• Semi-supervised spatially consistent charting