# Deep Learning Radio Frequency Signal Classification with Hybrid Images

arXiv: https://arxiv.org/abs/2105.09063 · HTML: https://mlsanity.com/paper/2105.09063/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels](https://mlsanity.com/paper/1911.04970/) (2019)
- [Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1703.00737/) (2017)
- [End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications](https://mlsanity.com/paper/1712.03987/) (2017)
- [Drone classification from RF fingerprints using deep residual nets](https://mlsanity.com/paper/2011.13663/) (2020)
- [Spectrum Monitoring for Radar Bands using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.00462/) (2017)
- [Multi-Label Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1804.04395/) (2018)
- [Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection](https://mlsanity.com/paper/1905.08054/) (2019)
- [RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks](https://mlsanity.com/paper/2009.05519/) (2020)

## Built on by (most cited first, 4 of 4)

- [DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches](https://mlsanity.com/paper/2502.18202/) (2025)
- [AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities](https://mlsanity.com/paper/2502.05315/) (2025)
- [T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge](https://mlsanity.com/paper/2401.04837/) (2024)
- [Deep Learning for Low-Latency, Quantum-Ready RF Sensing](https://mlsanity.com/paper/2404.17962/) (2024)

## Abstract (arXiv)

In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. In this work, we focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, we propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. Our initial results point out limitations to classical pre-processing approaches while also showing that it's possible to build a classifier that can leverage the strengths of multiple signal representations.