# Semi-Supervised Radio Signal Identification

arXiv: https://arxiv.org/abs/1611.00303 · HTML: https://mlsanity.com/paper/1611.00303/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2016.

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

- [Semi-Supervised RF Fingerprinting with Consistency-Based Regularization](https://mlsanity.com/paper/2304.14795/) (2023)
- [Deep Feature Learning for Wireless Spectrum Data](https://mlsanity.com/paper/2308.03530/) (2023)
- [Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition](https://mlsanity.com/paper/2311.08179/) (2023)

## Abstract (arXiv)

Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised learning strategies difficult and time consuming in practice. We demonstrate that semi-supervised learning techniques can be used to scale learning beyond supervised datasets, allowing for discerning and recalling new radio signals by using sparse signal representations based on both unsupervised and supervised methods for nonlinear feature learning and clustering methods.