RF Fingerprint Classification With Combinatorial-Randomness-Based Power Amplifiers and Convolutional Neural Networks: Secure analog/RF electronics and electromagnetics
RF Fingerprint Classification With Combinatorial-Randomness-Based Power Amplifiers and Convolutional Neural Networks: Secure analog/RF electronics and electromagnetics
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DOI：
10.1109/mssc.2022.3200302
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
V. Chen;Jiachen Xu;Yuyi Shen;Ethan Chen
中科院分区：
文献类型：
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作者：
V. Chen;Jiachen Xu;Yuyi Shen;Ethan Chen
物联网的发展需要比以往任何时候都更全面的安全措施。射频指纹（RFF）利用发射器物理层缺陷的信号和波形特征对设备进行分类和认证。为了防止模仿者的攻击，利用组合随机性通过物联网应用的高效PA来增强射频指纹。通过启用薄切片PA元件的不同子集，发射器可以重新配置220个子集，这些子集在边缘处显示出独特的RF指纹，用于信号分析。在这项工作中，基于组合随机性的PA在BLE系统中实现。以不同的信噪比收集从每种配置传输的BLE数据包的同相和正交采样，以模拟通信信道中的环境变化。轻量级卷积神经网络（CNN）分类器展示了在物联网环境中准确快速推断独特特征的可能性，我们的方法利用这些特征来实现片上时变射频指纹。
The growth of the IoT requires more comprehensive security measures than ever. RF fingerprinting (RFF) utilizes features in the signals and waveforms from transmitters’ physical-layer imperfections to classify and authenticate devices. To prevent attacks from impersonators, combinatorial randomness is exploited to augment the RF fingerprints with a high-efficiency PA for IoT applications. By enabling different subsets of thinly sliced PA elements, the transmitter can be reconfigured with 220 subsets that exhibit distinctive RF fingerprints for signal analysis at the edge. In this work, a combinatorial-randomness-based PA was implemented in a BLE system. The BLE packets’ in-phase and quadrature samples transmitted from each configuration are collected with different SNRs to emulate the environmental changes in communication channels. A lightweight convolutional neural network (CNN) classifier demonstrates the possibility of accurate and fast inference of unique features in the IoT environment, which our approach exploits to enable on-chip time-varying RF fingerprints.