AI 中文总结
本文提出监督设备图表化，利用5G NR CSI指纹生成低维可视化图表，通过NLER评估，实现设备识别与分布偏移分析。
AI 中文摘要
射频指纹识别（RFFI）是一种利用硬件引起的信号缺陷来区分物理无线设备的有前景的方法。传统的RFFI方法仅提供离散的设备标签，并且没有关于接收信号之间关系的人类可解释表示。我们提出了监督设备图表化，它将位置不敏感的信道状态信息（CSI）指纹映射到低维图表中，该图表可视化了簇紧凑性、重叠和异常值。我们使用来自六部商用智能手机的真实世界5G新无线电（5G NR）测量数据评估了该方法，并引入了邻居标签错误率（NLER）来量化类分离准确性。我们的结果表明，二维和三维设备图表提供了学习到的RFFI表示的可解释可视化。对于三维设备图表，同日测量的NLER为0.22%，次日测量的NLER为7.38%。设备图表揭示了跨日分布偏移，并将留出设备映射到同一型号的已知设备附近。增加设备图表的维度进一步改善了簇分离，但牺牲了可解释性。
英文摘要
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.
CommentsThis work has been submitted to the 2027 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
