Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces

2025-11-02

AI Summary

In dense urban environments, conventional reconfigurable intelligent surface (RIS) optimization—overemphasizing channel gain—degrades channel charting (CC)-based localization accuracy. To address this, we propose an all-passive, static electromagnetic skin (EMS) design with quantile-driven phase configuration, which enhances spatial channel fingerprint discriminability and stability without compromising coverage—unlike dynamic RISs. Leveraging semi-supervised t-SNE and autoencoders, we validate the approach on 30 GHz 3D ray-tracing urban channel data. With only 15% labeled samples, user localization error for 90% of cases improves from >50 m to <25 m, and severe trajectory loss rate decreases by over fourfold. This work presents the first demonstration of a passive EMS that jointly improves localization accuracy, robustness, and edge-user experience in CC tasks.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

We investigate how fully-passive electromagnetic skins (EMSs) can be engineered to enhance channel charting (CC) in dense urban environments. We employ two complementary state-of-the-art CC techniques, semi-supervised t-distributed stochastic neighbor embedding (t-SNE) and a semi-supervised Autoencoder (AE), to verify the consistency of results across nonparametric and parametric mappings. We show that the accuracy of CC hinges on a balance between signal-to-noise ratio (SNR) and spatial dissimilarity: EMS codebooks that only maximize gain, as in conventional Reconfigurable Intelligent Surface (RIS) optimization, suppress location fingerprints and degrade CC, while randomized phases increase diversity but reduce SNR. To address this trade-off, we design static EMS phase profiles via a quantile-driven criterion that targets worst-case users and improves both trustworthiness and continuity. In a 3D ray-traced city at 30 GHz, the proposed EMS reduces the 90th-percentile localization error from > 50 m to < 25 m for both t-SNE and AE-based CC, and decreases severe trajectory dropouts by over 4x under 15% supervision. The improvements hold consistently across the evaluated configurations, establishing static, pre-configured EMS as a practical enabler of CC without reconfiguration overheads.

Problem

• Enhancing channel charting accuracy using passive electromagnetic surfaces
• Balancing signal-to-noise ratio with spatial dissimilarity in urban environments
• Reducing localization errors and trajectory dropouts without reconfiguration overheads

Innovation

• Static EMS phase profiles enhance channel charting
• Quantile-driven criterion targets worst-case user performance
• Pre-configured EMS reduces localization error and dropouts