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] 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 (Datasets and usage and generation scripts can also be found there: https://wiki.cortexlab.fr/doku.php?id=tx-id.) 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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Acknowledgement
This work was supported by Inria Nokia Bell Labs ADR “Analytics and machine learning for mobile networks”. Experiments presented in this paper were carried out using the FIT/CorteXlab testbed (see http://www.cortexlab.fr).
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Morin, C., Cardoso, L.S., Hoydis, J., Gorce, JM., Vial, T. (2019). Transmitter Classification with Supervised Deep Learning. In: Kliks, A., et al. Cognitive Radio-Oriented Wireless Networks. CrownCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-030-25748-4_6
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