Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers' Nonlinearity Signatures

Jiachen Xu, Yuyi Shen, Ethan Chen, Vanessa Chen
2021 IEEE Open Journal of Circuits and Systems  
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
more » ... ayesian 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. INDEX TERMS Hardware security, Bayesian neural networks, radio frequency and wireless circuits, power amplifier, Gaussian random number generator, radio frequency fingerprint, Internet of Things.
doi:10.1109/ojcas.2021.3089499 fatcat:plswd32bezc3znc5dl22pxhmdi