Automatic Modulation Recognition Based on Single-channel Multi-scale Graph Neural Network

GUO Qiang; NIE Mengyun; QI Liangang; Kaliuzhnyi Mykola

doi:10.11999/JEIT220840

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1575-1584

GUO Qiang, NIE Mengyun, QI Liangang, Kaliuzhnyi Mykola. Automatic Modulation Recognition Based on Single-channel Multi-scale Graph Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1575-1584. doi: 10.11999/JEIT220840

Citation:

GUO Qiang, NIE Mengyun, QI Liangang, Kaliuzhnyi Mykola. Automatic Modulation Recognition Based on Single-channel Multi-scale Graph Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1575-1584. doi: 10.11999/JEIT220840

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Automatic Modulation Recognition Based on Single-channel Multi-scale Graph Neural Network

doi: 10.11999/JEIT220840

GUO Qiang^{1, 2},
NIE Mengyun^{1, 2},
QI Liangang^{1, 2
,
,},
Kaliuzhnyi Mykola^{1, 3}

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology, Harbin 150001, China
3.
Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine

Funds: The National Key R & D Plan(2018YFE0206500), The National Natural Science Foundation of China (62071140), The Fundamental Research Funds for Central Universities (3072022QBZ0801)

Received Date: 2022-06-24
Rev Recd Date: 2022-11-08

Available Online: 2022-11-10

Publish Date: 2023-05-10

Abstract

Abstract

Considering the shortcomings of the Adaptive Visibility Graph (AVG) algorithm being too complex and the accuracy improvement is not significant, an Automatic Modulation Recognition(AMR) framework based on Single-channel Multi-scale Graph Neural Network (SMGNN) is proposed and interpretability studies are conducted on the various parts of the framework. Firstly, the multi-layer perceptron and one-dimensional convolutional adaptive are used to realize the mapping between single-channel signal sequences and graphs, which reduces effectively the complexity of AVG algorithms. Secondly, a multi-scale graph neural network is designed to fuse the features of different resolutions, which improves the accuracy of model recognition. Experiments show that the SMGNN algorithm proposed in this paper saves nearly half of the parameter amount compared with the AVG algorithm, and the recognition accuracy has been greatly improved.
- Automatic Modulation Recognition(AMR),
- Graph Neural Network(GNN),
- Adaptive Visibility Graph(AVG),
- Feature Fusion(FF)

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References(23)

References

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