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

2026-09-21

AI Summary

研究提出了一种监督设备绘图方法，利用5G商用设备的CSI测量值生成低维图表，可视化设备信号关系，并通过NLER评估类分离精度。

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.

Problem

• Radio Frequency Fingerprint Identification
• Channel-State Information
• Cluster Visualization
• Device Charting

Innovation

• supervised device charting
• channel-state information (CSI)
• neighbor label error rate (NLER)
• 5G New Radio (5G NR)