A symmetric adaptive visibility graph classification method of orthogonal signals for automatic modulation classification

Visibility graph methods allow time series to mine non‐Euclidean spatial features of sequences by using graph neural network algorithms. Unlike the traditional fixed‐rule‐based univariate time series visibility graph methods, a symmetric adaptive visibility graph method is proposed using orthogonal...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IET communications Jg. 17; H. 10; S. 1208 - 1219
Hauptverfasser:	Bai, Haihai, Yang, Jingjing, Huang, Ming, Li, Wenting
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Stevenage John Wiley & Sons, Inc 01.06.2023 Wiley
Schlagworte:	Accuracy Adaptive algorithms Adaptive sampling adaptive signal processing Adaptive visibility graph Algorithms Benchmarks Classification Communication Datasets Deep learning Fading Feature extraction Fourier transforms Graph neural networks Graph representations Hypothesis testing Mapping Modulation modulation classification neural nets Neural networks non‐Euclidean space orthogonal signals Quadratures Radio signals Sequences Signal classification Time series Visibility Wavelet transforms
ISSN:	1751-8628, 1751-8636
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!