Power Amplifier enabled RF Fingerprint Identification

Yuepei Li; Yuan Ding; George Goussetis; Junqing Zhang

doi:10.1109/WMCS52222.2021.9493272

Power Amplifier enabled RF Fingerprint Identification

Yuepei Li, Yuan Ding, George Goussetis, Junqing Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

230 Downloads (Pure)

Abstract

The development of radio frequency fingerprint (RFF) identification technique aims to identify and classify wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm based on image recognition method is developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy rate exceeds 99%.

Original language	English
Title of host publication	2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS)
Publisher	IEEE
ISBN (Electronic)	9781665403092
DOIs	https://doi.org/10.1109/WMCS52222.2021.9493272
Publication status	Published - 27 Jul 2021
Event	2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems - , United States Duration: 18 May 2021 → 20 May 2021 https://texassymposium.org/

Conference

Conference	2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems
Country/Territory	United States
Period	18/05/21 → 20/05/21
Internet address	https://texassymposium.org/

Keywords

Convolution neural network (CNN)
power amplifier non-linearity
radio frequency fingerprint (RFF)

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Electrical and Electronic Engineering
Instrumentation

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10.1109/WMCS52222.2021.9493272

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@inproceedings{2b2818fb293946f492c2296698f65dc9,

title = "Power Amplifier enabled RF Fingerprint Identification",

abstract = "The development of radio frequency fingerprint (RFF) identification technique aims to identify and classify wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm based on image recognition method is developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy rate exceeds 99%.",

keywords = "Convolution neural network (CNN), power amplifier non-linearity, radio frequency fingerprint (RFF)",

author = "Yuepei Li and Yuan Ding and George Goussetis and Junqing Zhang",

note = "Funding Information: ACKNOWLEDGMENT Mr Yuepei Li{\textquoteright}s work was supported by Heriot-Watt University DTP scholarship. Dr Yuan Ding{\textquoteright}s work was supported by EPSRC (UK) under Grant EP/L020009/1 and EP/V002635/1. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems ; Conference date: 18-05-2021 Through 20-05-2021",

year = "2021",

month = jul,

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doi = "10.1109/WMCS52222.2021.9493272",

language = "English",

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publisher = "IEEE",

address = "United States",

url = "https://texassymposium.org/",

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TY - GEN

T1 - Power Amplifier enabled RF Fingerprint Identification

AU - Li, Yuepei

AU - Ding, Yuan

AU - Goussetis, George

AU - Zhang, Junqing

N1 - Funding Information: ACKNOWLEDGMENT Mr Yuepei Li’s work was supported by Heriot-Watt University DTP scholarship. Dr Yuan Ding’s work was supported by EPSRC (UK) under Grant EP/L020009/1 and EP/V002635/1. Publisher Copyright: © 2021 IEEE.

PY - 2021/7/27

Y1 - 2021/7/27

N2 - The development of radio frequency fingerprint (RFF) identification technique aims to identify and classify wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm based on image recognition method is developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy rate exceeds 99%.

AB - The development of radio frequency fingerprint (RFF) identification technique aims to identify and classify wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm based on image recognition method is developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy rate exceeds 99%.

KW - Convolution neural network (CNN)

KW - power amplifier non-linearity

KW - radio frequency fingerprint (RFF)

UR - http://www.scopus.com/inward/record.url?scp=85112113697&partnerID=8YFLogxK

U2 - 10.1109/WMCS52222.2021.9493272

DO - 10.1109/WMCS52222.2021.9493272

M3 - Conference contribution

BT - 2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS)

PB - IEEE

T2 - 2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems

Y2 - 18 May 2021 through 20 May 2021

ER -

Power Amplifier enabled RF Fingerprint Identification

Abstract

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Keywords

ASJC Scopus subject areas

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