Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures
Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures
复制标题
使用功率放大器的贝叶斯神经网络对射频发射器进行识别和分类 – 非线性特征
DOI：
10.1109/ojcas.2021.3089499
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发表时间：
2021
影响因子：
2.6
通讯作者：
Chen, Vanessa
中科院分区：
文献类型：
--
作者：
Xu, Jiachen;Shen, Yuyi;Chen, Ethan;Chen, Vanessa
新兴物联网（IoT）环境中的边缘设备需要全面的安全措施，这些措施必须在普适计算的功率预算范围内。在本文中，发射机识别方案组成的一个轻量级的贝叶斯神经网络（BNN）为基础的分类器，使用原始的时域数据。使用65 nm工艺设计套件（PDK）的高效率CMOS功率放大器设计的原理图级仿真中获得的数据进行评估。贝叶斯神经网络实现了89.5%的准确率分类六个发射机的任务。此外，BNN分类器在具有并行伪高斯随机数发生器的现场可编程门阵列（FPGA）上实现，以实现每秒超过340，000个分类的吞吐量，每个分类任务的平均能耗为0.548 μJ。这种低功耗系统可为能源受限的物联网设备和传感器提供全面的安全性。
The edge devices in an emerging Internet-of-Things (IoT) environment require comprehensive security measures that are within the power budget for ubiquitous computing. In this paper, a transmitter identification scheme consisting of a lightweight Bayesian neural network (BNN)-based classifier using raw time-domain data is presented. Evaluation is performed with data obtained in schematic-level simulation of high-efficiency CMOS power amplifier designs using a 65 nm process design kit (PDK). The Bayesian neural networks achieve 89.5% accuracy on the task of classifying six transmitters. Moreover, the BNN classifier is implemented on field-programmable gate array (FPGA) with parallel pseudo-Gaussian random number generators to achieve a throughput of more than 340,000 classifications per second, with average energy consumption for each classification task of 0.548 μJ. This low-power system enables comprehensive security for energy-constrained IoT devices and sensors.