# Unsupervised Representation Learning of Structured Radio Communication Signals

arXiv: https://arxiv.org/abs/1604.07078 · HTML: https://mlsanity.com/paper/1604.07078/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2016.

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

- [Multi-task Learning for Radar Signal Characterisation](https://mlsanity.com/paper/2306.13105/) (2023)
- [Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge](https://mlsanity.com/paper/2110.14357/) (2021)
- [Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation](https://mlsanity.com/paper/2501.03461/) (2025)
- [Wave-Informed Matrix Factorization with Global Optimality Guarantees](https://mlsanity.com/paper/2107.09144/) (2021)
- [Channel model for end-to-end learning of communications systems: A survey](https://mlsanity.com/paper/2204.03944/) (2022)
- [ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation](https://mlsanity.com/paper/2312.17446/) (2023)
- [Topological Neural Networks over the Air](https://mlsanity.com/paper/2502.10070/) (2025)

## Abstract (arXiv)

We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative met- rics for quality of encoding using domain relevant performance metrics.