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Transmitter Classification With Supervised Deep Learning

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Abstract

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.
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Dates and versions

hal-02132970 , version 1 (20-05-2019)

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Cyrille Morin, Leonardo Cardoso, Jakob Hoydis, Jean-Marie Gorce, Thibaud Vial. Transmitter Classification With Supervised Deep Learning. CROWNCOM 2019 - 14th EAI International conference on Cognitive Radio Oriented Wireless Networks, Jun 2019, Poznan, Poland. pp.1-14. ⟨hal-02132970⟩
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