  Devices authentication is one crucial aspect of any communication system.
Recently, the physical layer approach radio frequency (RF) fingerprinting has
gained increased interest as it provides an extra layer of security without
requiring additional components. In this work, we propose an RF fingerprinting
based transmitter authentication approach density trace plot (DTP) to exploit
device-identifiable fingerprints. By considering IQ imbalance solely as the
feature source, DTP can efficiently extract device-identifiable fingerprints
from symbol transition trajectories and density center drifts. In total, three
DTP modalities based on constellation, eye and phase traces are respectively
generated and tested against three deep learning classifiers: the 2D-CNN,
2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is
verified using a practical dataset collected from the ADALM-PLUTO
software-defined radios (SDRs).
