# Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems

arXiv: https://arxiv.org/abs/1605.00716 · HTML: https://mlsanity.com/paper/1605.00716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2016.

## Built on by (most cited first, 3 of 3)

- [Joint Signal Detection and Automatic Modulation Classification via Deep Learning](https://mlsanity.com/paper/2405.00736/) (2024)
- [RadioFlow: Efficient Radio Map Construction Framework with Flow Matching](https://mlsanity.com/paper/2510.09314/) (2025)
- [Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification](https://mlsanity.com/paper/2106.10543/) (2021)

## Abstract (arXiv)

We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization. Using this architecture we are able to outperform our prior results in accuracy vs signal to noise ratio against an identical system without attention, however we believe such an attention model has implication far beyond the task of modulation recognition.