An Investigation of Power Amplifier Feature Modeling and Generation Method for RF Fingerprint Identification

Radio Frequency Fingerprint Identification (RFFI) exploits inherent hardware imperfections in devices for identification, and hardware imperfections generate Radio Frequency Fingerprint (RFF) including various features. The nonlinearity of the power amplifier (PA) is an important feature of RFF that has been widely used in radio frequency identification (RFFI) technology. In this paper, we constructed 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 for the estimation of PA parameters. Using this testbed, we modeled and estimated PA parameters using 60 real IEEE 802.15.4 devices to obtain real PA parameters and generate synthetic PA parameters for generation of synthetic devices. When the number of devices requiring identification or research is limited, this method can be employed to expand the device sample size to conduct identification model training or related studies. Experimental results demonstrated the feasibility of the synthetic data-based pre-training approach, whose performance approaches that of trained models without pre-training. In practical applications, legal device identity registration in device identification can be achieved with a low-cost fine-tuning compared to retraining.