用静态电磁皮肤提升城市环境定位精度，降低误差超50%。
Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces
- 设计静态相位配置，平衡信噪比与空间差异性。
- 30GHz环境下定位误差从>50米降至<25米，轨迹丢失减少4倍以上。
- 无需动态调参，适合大规模部署的智能表面场景。
我们研究如何通过全被动电磁皮肤（EMS）增强密集城市环境中的信道图谱（CC）性能。采用半监督t-SNE与半监督自编码器（AE）两种方法验证结果一致性，发现信道图谱精度取决于信噪比（SNR）与空间差异性的平衡：仅最大化增益的传统可重构智能表面（RIS）优化会抑制位置指纹，降低定位效果；而随机相位虽提升多样性但削弱信噪比。为此，我们提出基于分位数驱动准则的静态相位设计，聚焦最差用户，提升可信度与连续性。在30GHz三维射线追踪城市模型中，该方法使t-SNE与AE-based CC的90百分位定位误差从超过50米降至低于25米，并在15%标注率下使严重轨迹丢失减少4倍以上。各类配置下表现一致，证实静态预配置EMS可无重配置开销实现高效信道图谱构建。
原文摘要 · Abstract (English)
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.
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