Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments

Mischa Vasylyev; Frederik Zumegen; Reinhard Wiesmayr; Christoph Studer

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arXiv · 2609.24213

Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments

Mischa Vasylyev·Frederik Zumegen·Reinhard Wiesmayr·Christoph Studer

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Abstract

Radio frequency fingerprint identification (RFFI) is a promising approach to distinguish physical wireless devices using hardware-induced signal imperfections. Conventional RFFI methods only provide discrete device labels and no human-interpretable representation of the relations among received signals. We propose supervised device charting, which maps location-insensitive channel-state information (CSI) fingerprints to a low-dimensional chart that visualizes cluster compactness, overlap, and outliers. We evaluate the method with real-world 5G New Radio (5G NR) measurements from six commercial smartphones and introduce the neighbor label error rate (NLER) to quantify class-separation accuracy. Our results demonstrate that two- and three-dimensional device charts provide an interpretable visualization of the learned RFFI representation. For three-dimensional device charts, the NLER is 0.22% for same-day measurements and 7.38% for measurements from the next day. The device charts reveal a cross-day distribution shift and map the held-out device close to the known device of the same model. Increasing the device chart dimensions further improves cluster separation at the expense of interpretability.

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Mischa Vasylyev, Frederik Zumegen, Reinhard Wiesmayr, Christoph Studer. 2026-09-21. Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments. https://arxiv.org/abs/2609.24213

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