SigCLR: A contrastive learning approach to unsupervised modulation recognition and novelty detection
Year: 2025
Type: conference-paper
Abstract: We introduce a contrastive learning architecture for radio datasets called SigCLR. We show how this method of unsupervised machine learning can be applied for a variety of tasks including but not limited to modulation recognition and novelty detection. We show results for a functioning modulation class detector, where an unknown signal can be passed into the trained network and it can be compared to a number of known modulations.
DOI: 10.22323/1.471.0025
Authors: Nicholas Bruce, Belaid Moa, Stephen Harrison, Peter F. Driessen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4408869433