# Classification of Radio Signals and HF Transmission Modes with Deep Learning

arXiv: https://arxiv.org/abs/1906.04459 · HTML: https://mlsanity.com/paper/1906.04459/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

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

- [Countermeasures Against Adversarial Examples in Radio Signal Classification](https://mlsanity.com/paper/2407.06796/) (2024)
- [A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification](https://mlsanity.com/paper/2407.06807/) (2024)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [RF Signal Classification with Synthetic Training Data and its Real-World Performance](https://mlsanity.com/paper/2206.12967/) (2022)
- [Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals](https://mlsanity.com/paper/2410.18283/) (2024)
- [Large-Scale Classification of Shortwave Communication Signals with Machine Learning](https://mlsanity.com/paper/2504.05455/) (2025)
- [A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification](https://mlsanity.com/paper/2506.11901/) (2025)

## Abstract (arXiv)

This paper investigates deep neural networks for radio signal classification. Instead of performing modulation recognition and combining it with further analysis methods, the classifier operates directly on the IQ data of the signals and outputs the transmission mode. A data set of radio signals of 18 different modes, that commonly occur in the HF radio band, is presented and used as a showcase example. The data set considers HF channel properties and is used to train four different deep neural network architectures. The results of the best networks show an excellent accuracy of up to 98%.