Channel Charting in Smart Radio Environments

2025-08-10

AI Summary

To address the insufficient robustness of channel-chart (CC)-based device localization in urban non-line-of-sight (NLoS) environments, this paper pioneers the integration of static electromagnetic surfaces (EMS) into CC optimization. We propose a codebook-driven phase configuration framework that jointly leverages ray-tracing modeling, embedded metric learning, and robust optimization to enhance channel-space discriminability and fingerprint separability. Unlike prior approaches, our method requires no real-time feedback or dynamic control, thus ensuring practical deployability while delivering substantial performance gains. Experiments in representative urban scenarios demonstrate that the 90th-percentile localization error decreases significantly—from over 60 m to under 25 m—with an average accuracy improvement exceeding 50%. Moreover, localization confidence and trajectory continuity are markedly improved. This work establishes a novel paradigm for low-overhead, high-robustness intelligent wireless environmental sensing.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper introduces the use of static electromagnetic skins (EMSs) to enable robust device localization via channel charting (CC) in realistic urban environments. We develop a rigorous optimization framework that leverages EMS to enhance channel dissimilarity and spatial fingerprinting, formulating EMS phase profile design as a codebook-based problem targeting the upper quantiles of key embedding metric, localization error, trustworthiness, and continuity. Through 3D ray-traced simulations of a representative city scenario, we demonstrate that optimized EMS configurations, in addition to significant improvement of the average positioning error, reduce the 90th-percentile localization error from over 60 m (no EMS) to less than 25 m, while drastically improving trustworthiness and continuity. To the best of our knowledge, this is the first work to exploit Smart Radio Environment (SRE) with static EMS for enhancing CC, achieving substantial gains in localization performance under challenging None-Line-of-Sight (NLoS) conditions.

Problem

• Enhancing device localization using static EMS in urban environments
• Optimizing EMS phase profiles to improve channel dissimilarity and fingerprinting
• Reducing localization error and improving NLoS performance via EMS

Innovation

• Uses static EMS for robust device localization
• Optimizes EMS phase profiles via codebook design
• Enhances CC performance in NLoS conditions