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The imperfections in the RF frontend of different transmitters can be used todistinguish them. This process is called transmitter identification using RFfingerprints. The nonlinearity in the power amplifier of the RF frontend is asignificant cause of the discrepancy in RF fingerprints, which enablestransmitter identification. In this work, we use deep learning to identifydifferent transmitters using their nonlinear characteristics. By developing anonlinear model generator based on extensive measurements, we were able toextend the evaluation of transmitter identification to include a larger numberof transmitters beyond what exists in the literature. We were also able tostudy the impact of transmitter variability on identification accuracy.Additionally, many other factors were considered including modulation type,length of data used for identification, and type of data being transmittedwhether identical or random under a realistic channel model. Simulation resultswere compared with experiments which confirmed similar trends.

Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity