  The imperfections in the RF frontend of different transmitters can be used to
distinguish them. This process is called transmitter identification using RF
fingerprints. The nonlinearity in the power amplifier of the RF frontend is a
significant cause of the discrepancy in RF fingerprints, which enables
transmitter identification. In this work, we use deep learning to identify
different transmitters using their nonlinear characteristics. By developing a
nonlinear model generator based on extensive measurements, we were able to
extend the evaluation of transmitter identification to include a larger number
of transmitters beyond what exists in the literature. We were also able to
study 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 transmitted
whether identical or random under a realistic channel model. Simulation results
were compared with experiments which confirmed similar trends.
