TY - JOUR
T1 - RFFI Protocols Using Antenna Mutual Coupling and Power Amplifier Nonlinear Memory Effects
AU - Li, Yuepei
AU - Podilchak, Symon
AU - Zhang, Junqing
AU - Cotton, Simon L.
AU - Ratnarajah, Tharmalingam
AU - Ding, Yuan
PY - 2025/4/8
Y1 - 2025/4/8
N2 - This paper presents a radio frequency fingerprinting identification (RFFI) protocol in wireless links with multi-antenna array transmitters. Multi-antenna systems are widely used in wireless communication systems for diversity and/or multiplexing. The mutual coupling (MC) effects arising from electromagnetic interactions between adjacent array elements can influence the RF characteristics of the transmitter and eventually the performance of the established links. In this paper, a novel RFF strategy is proposed to expand the differences in the RFF characteristics among wireless devices from the same vendor, with the goal of massively improving RFF classification accuracy in low to medium signal-to-noise ratio (SNR) channel conditions. Experimental results show that when classifying six power amplifiers (PAs) from the same vendor, 21% to 62% average classification accuracy improvement can be achieved by enlarging the RFF feature differences arising from the PA nonlinearity arising from the array coupling.
AB - This paper presents a radio frequency fingerprinting identification (RFFI) protocol in wireless links with multi-antenna array transmitters. Multi-antenna systems are widely used in wireless communication systems for diversity and/or multiplexing. The mutual coupling (MC) effects arising from electromagnetic interactions between adjacent array elements can influence the RF characteristics of the transmitter and eventually the performance of the established links. In this paper, a novel RFF strategy is proposed to expand the differences in the RFF characteristics among wireless devices from the same vendor, with the goal of massively improving RFF classification accuracy in low to medium signal-to-noise ratio (SNR) channel conditions. Experimental results show that when classifying six power amplifiers (PAs) from the same vendor, 21% to 62% average classification accuracy improvement can be achieved by enlarging the RFF feature differences arising from the PA nonlinearity arising from the array coupling.
KW - Antenna mutual coupling
KW - convolution neural network (CNN)
KW - nonlinear memory effect
KW - radio frequency fingerprinting (RFF)
UR - http://www.scopus.com/inward/record.url?scp=105002438005&partnerID=8YFLogxK
U2 - 10.1109/lcomm.2025.3558555
DO - 10.1109/lcomm.2025.3558555
M3 - Article
SN - 1089-7798
JO - IEEE Communications Letters
JF - IEEE Communications Letters
ER -