# Large-Scale Classification of Shortwave Communication Signals with Machine Learning

arXiv: https://arxiv.org/abs/2504.05455 · HTML: https://mlsanity.com/paper/2504.05455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Large Scale Radio Frequency Signal Classification](https://mlsanity.com/paper/2207.09918/) (2022)
- [Classification of Radio Signals and HF Transmission Modes with Deep Learning](https://mlsanity.com/paper/1906.04459/) (2019)
- [RF Signal Classification with Synthetic Training Data and its Real-World Performance](https://mlsanity.com/paper/2206.12967/) (2022)

## Built on by (most cited first, 1 of 1)

- [RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study](https://mlsanity.com/paper/2610.15494/) (2026)

## Abstract (arXiv)

This paper presents a deep learning approach to the classification of 160 shortwave radio signals. It addresses the typical challenges of the shortwave spectrum, which are the large number of different signal types, the presence of various analog modulations and ionospheric propagation. As a classifier a deep convolutional neural network is used, that is trained to recognize 160 typical shortwave signal classes. The approach is blind and therefore does not require preknowledge or special preprocessing of the signal and no manual design of discriminative features for each signal class. The network is trained on a large number of synthetically generated signals and high quality recordings. Finally, the network is evaluated on real-world radio signals obtained from globally deployed receiver hardware and achieves up to 90% accuracy for an observation time of only 1 second.