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...
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| Vydáno v: | IET communications Ročník 17; číslo 10; s. 1208 - 1219 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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John Wiley & Sons, Inc
01.06.2023
Wiley |
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| ISSN: | 1751-8628, 1751-8636 |
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| 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.
A novel visibility graph classification method of radio modulated signal is proposed in this work, which consists of a Quadrature Signal Symmetric Adaptive Visibility Graph (QSSAVG) algorithm and an end‐to‐end framework of Orthogonal Signal Graph Classification Network (QSGCNet). Extensive experimental results on two benchmark datasets and a simulated dataset containing channel fading show that the proposed QSSAVG method in this paper outperforms the benchmark method in classification accuracy and is also more robust against channel fading and noise variations. |
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| AbstractList | 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. 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. A novel visibility graph classification method of radio modulated signal is proposed in this work, which consists of a Quadrature Signal Symmetric Adaptive Visibility Graph (QSSAVG) algorithm and an end‐to‐end framework of Orthogonal Signal Graph Classification Network (QSGCNet). Extensive experimental results on two benchmark datasets and a simulated dataset containing channel fading show that the proposed QSSAVG method in this paper outperforms the benchmark method in classification accuracy and is also more robust against channel fading and noise variations. 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. |
| Author | Yang, Jingjing Li, Wenting Bai, Haihai Huang, Ming |
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| Snippet | Visibility graph methods allow time series to mine non‐Euclidean spatial features of sequences by using graph neural network algorithms. Unlike the traditional... Abstract Visibility graph methods allow time series to mine non‐Euclidean spatial features of sequences by using graph neural network algorithms. Unlike the... |
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| SubjectTerms | 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 |
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| Title | A symmetric adaptive visibility graph classification method of orthogonal signals for automatic modulation classification |
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