A new RF fingerprint identification method based on preamble of signal

被引：0
|
作者：
Zeng S. [1 ]
Zhu F. [1 ]
Yang J. [2 ]
机构：
[1] Combat Support Academy, Rocket Force University of Engineering, Xi’an
[2] Missile Engineering Academy, Rocket Force University of Engineering, Xi’an
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2022年 / 48卷 / 12期
基金：
中国国家自然科学基金;
关键词：
convolutional neural network; deep learning; preamble extraction; radio frequency fingerprint identification; wireless communication;
D O I：
10.13700/j.bh.1001-5965.2021.0164
中图分类号：
学科分类号：
摘要：
Deep learning-based RF fingerprint recognition methods now primarily use raw data samples as the input of the network, never taking into account how the signal’ s content affects classification outcomes, and the structure of the network is relatively simple. In response to the above problems, the preamble of the signal as the input of the network was studied and we proposed a new preamble extraction algorithm. We extracted the preamble of 10 ADALM-PLUTO software-defined radios (SDR) and built the preamble data sets at three different distances. The Inception network structure is proposed to be used in RF fingerprint identification in this paper, and the classification accuracy is still 98.58% under the wireless transmission distance of 10 m. The classification accuracy is increased as compared to the pre-existing convolutional neural network (CNN) built on the AlexNet network. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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页码：2566 / 2575
页数：9
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