Deep radio signal clustering with interpretability analysis based on saliency map

With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situat...

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Veröffentlicht in:	Digital communications and networks Jg. 10; H. 5; S. 1448 - 1458
Hauptverfasser:	Zhou, Huaji, Bai, Jing, Wang, Yiran, Ren, Junjie, Yang, Xiaoniu, Jiao, Licheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2024 KeAi Communications Co., Ltd
Schlagworte:	Autoencoder Clustering features visualization Deep learning interpretability Unsupervised radio signal clustering Clustering features visualization Unsupervised radio signal clustering Autoencoder Deep learning interpretability
ISSN:	2352-8648, 2352-8648
Online-Zugang:	Volltext
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