# Large Scale Radio Frequency Signal Classification

arXiv: https://arxiv.org/abs/2207.09918 · HTML: https://mlsanity.com/paper/2207.09918/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)

## Built on by (most cited first, 8 of 8)

- [RF-GPT: Teaching AI to See the Wireless World](https://mlsanity.com/paper/2602.14833/) (2026)
- [Large Scale Radio Frequency Wideband Signal Detection & Recognition](https://mlsanity.com/paper/2211.10335/) (2022)
- [Large-Scale Classification of Shortwave Communication Signals with Machine Learning](https://mlsanity.com/paper/2504.05455/) (2025)
- [Deep-Learned Compression for Radio-Frequency Signal Classification](https://mlsanity.com/paper/2403.03150/) (2024)
- [Digital Operating Mode Classification of Real-World Amateur Radio Transmissions](https://mlsanity.com/paper/2501.07337/) (2025)
- [A look at adversarial attacks on radio waveforms from discrete latent space](https://mlsanity.com/paper/2506.09896/) (2025)
- [Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals](https://mlsanity.com/paper/2602.13556/) (2026)
- [Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization](https://mlsanity.com/paper/2610.12142/) (2026)

## Abstract (arXiv)

Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. TorchSig incorporates data handling principles that are common to the vision domain, and it is meant to serve as an open-source foundation for future signals machine learning research. Initial experiments using the Sig53 dataset are conducted using state of the art (SoTA) convolutional neural networks (ConvNets) and Transformers. These experiments reveal Transformers outperform ConvNets without the need for additional regularization or a ConvNet teacher, which is contrary to results from the vision domain. Additional experiments demonstrate that TorchSig's domain-specific data augmentations facilitate model training, which ultimately benefits model performance. Finally, TorchSig supports on-the-fly synthetic data creation at training time, thus enabling massive scale training sessions with virtually unlimited datasets.