Radio Frequency Fingerprinting (RFF) is the attribution of uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers. RFF approaches relying on pre-determined expert features lack generalizability, and state-of-the-art approaches based on Convolutional Neural Networks (CNNs) can be too demanding for endpoint devices to train. This work presents Fingerprint Extraction through Distortion Reconstruction (FEDR), a best-of-both-worlds technique which employs a pre-trained CNN to identify and extract a small, salient set of unique features, amenable for use in lightweight machine learning models. Given a received distorted signal, the FEDR network encodes signal distortions into “fingerprints,” which can be used by lightweight ML classifiers to perform RFF with minimal resource consumption at the endpoint. FEDR learns by transforming generated signals into reconstructions of received signals, relying solely on the fingerprints as representations of the distortions – as the reconstructions improve, the fingerprints better encode the distortions. The FEDR technique was evaluated on synthetic IQ-imbalanced IEEE 802.11a/g data, where FEDR fingerprints were shown to encode actual IQ imbalance parameters, signifying successful isolation of distortion information and validating the FEDR technique. FEDR was further evaluated on a representative real-world WiFi dataset, where extracted fingerprints were coupled with a lightweight two-layer dense network. When compared against two common RFF techniques, the FEDR-based approach achieved state-of-the-art performance with Matthews Correlation Coefficient ranging from 0.984 (5 classes) to 0.851 (100 classes), using nearly 73% fewer training parameters than the next-best technique.

中文翻译：

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通过失真重建 （FEDR） 进行指纹提取：一种基于 CNN 的射频指纹识别方法


射频指纹识别 （RFF） 是通过机器学习 （ML） 分类器将唯一可识别的信号失真归因于发射机。依赖于预先确定的专家特征的 RFF 方法缺乏通用性，而基于卷积神经网络 （CNN） 的最新方法对于端点设备来说可能要求太高，无法训练。这项工作提出了通过失真重建 （FEDR） 进行指纹提取，这是一种两全其美的技术，它采用预先训练的 CNN 来识别和提取一小组突出的独特特征，适合在轻量级机器学习模型中使用。给定接收到的失真信号，FEDR 网络将信号失真编码为“指纹”，轻量级 ML 分类器可以使用这些指纹来执行 RFF，同时在端点消耗最少的资源。FEDR 通过将生成的信号转换为接收信号的重建来学习，仅依靠指纹作为失真的表示 - 随着重建的改进，指纹可以更好地编码失真。FEDR 技术在合成 IQ 不平衡的 IEEE 802.11a/g 数据上进行评估，其中 FEDR 指纹显示对实际的 IQ 不平衡参数进行编码，表明成功隔离了失真信息并验证了 FEDR 技术。FEDR 在具有代表性的真实世界 WiFi 数据集上进行了进一步评估，其中提取的指纹与轻量级两层密集网络相结合。与两种常见的 RFF 技术相比，基于 FEDR 的方法实现了最先进的性能，Matthews 相关系数范围为 0.984（5 个类别）到 0.851（100 个类别），使用的训练参数比次优技术少了近 73%。