Clustering Method for Signals in the Wideband RF Spectrum Using Semi-Supervised Deep Contrastive Learning

Clustering Method for Signals in the Wideband RF Spectrum Using Semi-Supervised Deep Contrastive Learning

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically,...

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Bibliographic Details
Main Authors: Adam Olesiński, Zbigniew Piotrowski
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Applied Sciences
Subjects:
RFML
radio frequency machine learning
unsupervised deep learning
AI
contrastive loss
cognitive radio
Online Access:https://www.mdpi.com/2076-3417/14/7/2990

Internet

https://www.mdpi.com/2076-3417/14/7/2990

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