Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces
This work addresses localization accuracy for wireless communication systems in urban settings, representing an incremental improvement by optimizing static surfaces rather than reconfigurable ones.
The paper tackled the problem of enhancing channel charting accuracy in dense urban environments by designing static electromagnetic skins, showing that the proposed approach reduces the 90th-percentile localization error from over 50 m to under 25 m and decreases severe trajectory dropouts by over 4x.
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.