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 $0.548~\mu J$ . This low-power system enables comprehensive security for energy-constrained IoT devices and sensors.

中文翻译：

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使用功率放大器非线性特征的贝叶斯神经网络对射频发射器进行识别和分类


新兴物联网 (IoT) 环境中的边缘设备需要在普适计算的功率预算范围内采取全面的安全措施。本文提出了一种发射机识别方案，该方案由使用原始时域数据的基于轻量级贝叶斯神经网络（BNN）的分类器组成。使用 65 nm 工艺设计套件 (PDK) 对高效 CMOS 功率放大器设计进行原理图级仿真中获得的数据进行评估。贝叶斯神经网络在对 6 个发射机进行分类的任务上实现了 89.5% 的准确率。此外，BNN分类器在具有并行伪高斯随机数生成器的现场可编程门阵列（FPGA）上实现，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。