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- Title
A symmetric adaptive visibility graph classification method of orthogonal signals for automatic modulation classification.
- Authors
Bai, Haihai; Yang, Jingjing; Huang, Ming; Li, Wenting
- Abstract
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 signals, a method applicable to in‐phase and quadrature (I/Q) orthogonal signals for adaptive graph mapping for radio modulated signals in automatic modulation classification tasks. The method directly models the intra‐channel and inter‐channel graph relations of I/Q signals using two different types of convolutional kernels. It captures non‐Euclidean spatial feature information of I/Q signals using a graph neural network combining graph sampling aggregation and graph differentiable pooling as a feature extractor. Extensive experimental results on two benchmark datasets and a simulated dataset containing channel fading show that the proposed Quadrature Signal Symmetric Adaptive Visibility Graph (QSSAVG) method in this paper outperforms the benchmark method in terms of classification accuracy and is also more robust against channel fading and noise variations.
- Subjects
AUTOMATIC classification; ADAPTIVE signal processing; TIME series analysis; CLASSIFICATION
- Publication
IET Communications (Wiley-Blackwell), 2023, Vol 17, Issue 10, p1208
- ISSN
1751-8628
- Publication type
Article
- DOI
10.1049/cmu2.12608