The growing number of wireless devices increases the need for secure network access. Radio Frequency Fingerprinting (RFF), a physical-layer authentication method, offers a promising solution as it requires no cryptography and resists spoofing. However, existing RFF approaches often lack a unified theory and effective feature extraction. Many methods use handcrafted signal features or direct neural network classification, leading to limited generalization and interpretability. In this work, we model the transmitter as a black box and analyze its impact on transmitted signals. By treating the deviation from an ideal signal as hardware-induced distortion, we represent the received signal using a Volterra series, using its kernels to capture linear and nonlinear hardware traits. To manage the high dimensionality of these kernels, we approximate them via wavelet decomposition and estimate coefficients through least-squares fitting. The resulting wavelet coefficients provide compact yet informative hardware representations, which are classified using a complex-valued neural network. Experiments on a public LoRa dataset show state-of-the-art performance, with over 98% accuracy in static channels and above 90% under multipath and Doppler effects. The proposed approach improves both interpretability and generalization across varying channel conditions.

翻译：无线设备数量的增长增加了对安全网络接入的需求。射频指纹识别作为一种物理层认证方法，因其无需密码学且能抵抗欺骗攻击而成为一种有前景的解决方案。然而，现有射频指纹识别方法通常缺乏统一的理论框架和有效的特征提取机制。许多方法采用手工设计的信号特征或直接的神经网络分类，导致泛化能力和可解释性受限。本研究将发射机建模为黑箱系统，分析其对发射信号的影响。通过将实际信号与理想信号的偏差视为硬件引入的失真，我们采用Volterra级数表示接收信号，利用其核函数捕捉硬件的线性和非线性特性。为处理这些核函数的高维性问题，我们通过小波分解对其进行近似，并采用最小二乘拟合估计系数。所得的小波系数提供了紧凑且信息丰富的硬件表征，随后使用复值神经网络进行分类。在公开LoRa数据集上的实验表明，该方法取得了最先进的性能：在静态信道中准确率超过98%，在多径和多普勒效应下仍能保持90%以上的准确率。所提出的方法在提升可解释性的同时，增强了不同信道条件下的泛化能力。