# Transmitter Classification With Supervised Deep Learning

arXiv: https://arxiv.org/abs/1905.07923 · HTML: https://mlsanity.com/paper/1905.07923/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 7 of 7)

- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [Class-Incremental Learning for Wireless Device Identification in IoT](https://mlsanity.com/paper/2105.06381/) (2021)
- [A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data](https://mlsanity.com/paper/2109.03784/) (2021)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges](https://mlsanity.com/paper/2410.23949/) (2024)
- [Enhancing Wireless Device Identification through RF Fingerprinting: Leveraging Transient Energy Spectrum Analysis](https://mlsanity.com/paper/2506.17439/) (2025)

## Abstract (arXiv)

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.