  Hardware imperfections in RF transmitters introduce features that can be used
to identify a specific transmitter amongst others. Supervised deep learning has
shown good performance in this task but using datasets not applicable to real
world situations where topologies evolve over time. To remedy this, the work
rests on a series of datasets gathered in the Future Internet of Things /
Cognitive Radio Testbed [4] (FIT/CorteXlab) to train a convolutional neural
network (CNN), where focus has been given to reduce channel bias that has
plagued previous works and constrained them to a constant environment or to
simulations. The most challenging scenarios provide the trained neural network
with resilience and show insight on the best signal type to use for
identification , namely packet preamble. The generated datasets are published
on the Machine Learning For Communications Emerging Technologies Initiatives
web site 4 in the hope that they serve as stepping stones for future progress
in the area. The community is also invited to reproduce the studied scenarios
and results by generating new datasets in FIT/CorteXlab.
