Radio frequency fingerprint identification (RFFI) is a promising physical layer authentication technique that utilizes the unique impairments within the analog front-end of transmitters as distinct identifiers. State-of-the-art RFFI systems are frequently powered by deep learning, which requires extensive training data to ensure satisfactory performance. However, current RFFI studies suffer from a severe lack of training data, which poses challenges in achieving high identification accuracy. In this paper, we propose a federated RFFI system that is particularly suitable for Internet of Things (IoT) networks, which holds a high potential to address the data scarcity challenge in RFFI development. Specifically, all the receivers in an IoT network can pre-train a deep learning-driven feature extractor in a federated and unsupervised manner. Subsequently, a new client can perform fine-tuning on the basis of the pre-trained feature extractor to activate its RFFI functionality. Extensive experimental evaluation was carried out, involving 60 commercial off-the-shelf (COTS) LoRa transmitters and six software-defined radio (SDR) receivers. The experimental results demonstrate that the federated RFFI protocol can effectively improve the identification accuracy from 63% to 95%, and is robust to receiver hardware and location variations.

中文翻译：

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由无监督对比学习提供支持的联合射频指纹识别


射频指纹识别 (RFFI) 是一种很有前途的物理层身份验证技术，它利用发射机模拟前端内的独特损伤作为独特的标识符。最先进的 RFFI 系统通常由深度学习提供支持，这需要大量的培训数据才能确保令人满意的性能。然而，目前的RFFI研究严重缺乏训练数据，这给实现高识别精度带来了挑战。在本文中，我们提出了一种特别适合物联网（IoT）网络的联合 RFFI 系统，该系统在解决 RFFI 开发中的数据稀缺挑战方面具有很大潜力。具体来说，物联网网络中的所有接收器都可以以联合且无监督的方式预训练深度学习驱动的特征提取器。随后，新客户端可以在预先训练的特征提取器的基础上进行微调，以激活其 RFFI 功能。进行了广泛的实验评估，涉及 60 个商用现成 (COTS) LoRa 发射器和六个软件定义无线电 (SDR) 接收器。实验结果表明，联邦RFFI协议可以有效地将识别精度从63%提高到95%，并且对接收器硬件和位置变化具有鲁棒性。