Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces

TL;DR

This paper demonstrates that static electromagnetic skins can significantly improve channel charting accuracy in urban environments by balancing gain and diversity, reducing localization errors and trajectory dropouts without reconfiguration.

Contribution

It introduces a static EMS phase profile design method that enhances channel charting performance, avoiding the need for reconfigurable surfaces.

Findings

Reduces 90th-percentile localization error from >50m to <25m

Decreases trajectory dropouts by over 4x

Consistent improvements across CC techniques and configurations

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.