Radio frequency fingerprint (RFF) has been utilized to mitigate spoofing attacks in open wireless environments, making use of the inherent characteristics of hardware. However, most existing RFF technologies are data-dependent, e.g., based on preambles or synchronization sequences. In this letter, we propose a novel data-independent RFF extraction scheme, called Least mean square-based Adaptive Filter and Stacking, abbreviated as LAFS, that is implemented on random data segments, like communication data. Intuitively, we extract converged tap coefficients as RFF by minimizing the divergence between the desired signal and the demodulated reference signal. To further improve the effect, we introduce a tap coefficient stacking (TSC) technique to stabilize the RFF. Our experiment on ZigBee devices shows that the proposed LAFS method successfully identifies transmitters with 98.9% accuracy at 10 dB by stacking 25 times.

中文翻译：

---

一种数据无关的射频指纹提取方案


射频指纹 (RFF) 利用硬件的固有特性，已被用来减轻开放无线环境中的欺骗攻击。然而，大多数现有的RFF技术是数据相关的，例如基于前导码或同步序列。在这封信中，我们提出了一种新颖的与数据无关的 RFF 提取方案，称为基于最小均方的自适应滤波器和堆栈，缩写为 LAFS，该方案在随机数据段（如通信数据）上实现。直观上，我们通过最小化所需信号和解调参考信号之间的差异来提取收敛抽头系数作为 RFF。为了进一步改善效果，我们引入了抽头系数叠加（TSC）技术来稳定 RFF。我们在 ZigBee 设备上的实验表明，所提出的 LAFS 方法通过叠加 25 次，成功识别了发射机，在 10 dB 下的准确率为 98.9%。