An Investigation of Power Amplifier Feature for Deep Learning Based RF Fingerprint Identification


Jing, Wentao, Peng, Linning, Zhang, Junqing ORCID: 0000-0002-3502-2926 and Fu, Hua (2025) An Investigation of Power Amplifier Feature for Deep Learning Based RF Fingerprint Identification. In: IEEE INFOCOM 2025 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2025-5-19 - 2025-5-19.

[thumbnail of INFOCOM2025WS_RFFI_PA.pdf] Text
INFOCOM2025WS_RFFI_PA.pdf - Author Accepted Manuscript
Available under License Creative Commons Attribution.
Download (1MB) | Preview

Abstract

Radio frequency fingerprint identification (RFFI) employs unique hardware impairments for device authentication. Power amplifier (PA) nonlinearity is an important hardware impairment feature, which has been widely used for RFFI. In this paper, we modeled and estimated PA parameters and studied their effects on RFFI. In particular, a memory polynomial model was used to capture the detailed features of PA, involving polynomial order, memory depth and coefficient matrix. These parameters are twisted and we designed grid search and deep learning-based algorithms to find optimal parameters, with a tradeoff of computational complexity and RFFI classification accuracy. We created a testbed consisting of 60 IEEE 802.15.4 devices and a universal software radio peripheral (USRP) X310 software-defined radio (SDR) platform as the receiver. The PA function of our devices can be disabled when needed, which allowed us to generate signals with and without PA features. Experimental results demonstrated that our proposed algorithms can estimate the PA parameters. Simulated signals with the estimated PA parameters were also demonstrated to retain the PA features accurately.

Item Type: Conference Item (Unspecified)
Uncontrolled Keywords: 4605 Data Management and Data Science, 46 Information and Computing Sciences, 40 Engineering, Bioengineering, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence
Divisions: Faculty of Science and Engineering
Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 04 Mar 2025 08:29
Last Modified: 03 Oct 2025 17:22
DOI: 10.1109/infocomwkshps65812.2025.11152961
Related Websites:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3190643
Altmetric
CORE (COnnecting REpositories)