Network operator identification through RF fingerprinting of base stations<bold> </bold>

被引：0

作者：

Lavoie, Philippe ^{[1
]}

Naboulsi, Diala ^{[2
]}

Gagnon, Francois ^{[1
]}

机构：

[1] Ecole Technol Super Montreal, Dept Genie Elect, Montreal, PQ, Canada

[2] Ecole Technol Super Montreal, Dept Genie Log & Technol Informat, Montreal, PQ, Canada

来源：

2024 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, CCECE 2024 | 2024年

关键词：

Radio frequency fingerprinting (RFF); complexvalued convolutional neural network (CNN); 4G/5G physical layer; false base station detection<bold>; </bold>;

D O I：

10.1109/CCECE59415.2024.10667133

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this work, we aim to identify network operators through radio frequency fingerprinting (RFF) of base stations (BSs). We propose to do so based on the classification of synchronization signals, using a complex-valued convolutional neural network (CNN) architecture. Our results indicate that LTE operators can be identified from raw in-phase and quadrature (I/Q) samples collected over a live network with an accuracy of more than 96%.<bold> </bold>

引用

页码：182 / 183

页数：2

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