Transmitter Classification With Supervised Deep Learning
This work addresses the problem of transmitter classification for IoT and cognitive radio systems, but it is incremental as it builds on existing deep learning methods with new datasets to reduce channel bias.
The paper tackles the problem of identifying RF transmitters using supervised deep learning by addressing dataset limitations in real-world scenarios with evolving topologies, resulting in a trained CNN that shows resilience in challenging conditions and identifies packet preamble as the best signal type for identification.
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.