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Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Xiaohui Yao1, Honghui Yang1, Meiping Sheng1

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.

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Summary
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This study introduces a robust method for automatic modulation classification (AMC) in underwater acoustic signals using graph convolution networks (GCN). The approach enhances signal analysis in challenging marine environments, improving communication system understanding.

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Underwater Acoustics

Background:

  • Automatic modulation classification (AMC) is crucial for analyzing underwater acoustic communication signals, particularly for defense and marine applications.
  • Existing feature extraction and deep learning methods struggle with the complexities of underwater acoustic channels, limiting classification accuracy.
  • The need for robust AMC methods is critical for accurately identifying enemy communication systems in challenging marine environments.

Purpose of the Study:

  • To enhance the stability and robustness of automatic modulation classification (AMC) in complex underwater acoustic channels.
  • To develop a novel method that fuses multi-domain features and deep features for improved modulation classification.
  • To leverage graph convolution networks (GCN) for processing structured information within acoustic signal data.

Main Methods:

  • A feature graph was constructed based on feature properties.
  • Multi-domain and deep features were extracted from received underwater acoustic signals using a deep neural network.
  • Graph convolution networks (GCN) were employed to fuse these multi-domain and deep features, followed by classification using a softmax layer.

Main Results:

  • The proposed GCN-based AMC method demonstrated significant performance improvements over state-of-the-art techniques.
  • Experiments on both simulated and real-world datasets validated the effectiveness of the approach.
  • The method proved to be robust and stable even in challenging underwater acoustic channel conditions.

Conclusions:

  • The fusion of multi-domain and deep features using GCN offers a powerful solution for robust AMC in underwater acoustic communications.
  • This approach effectively addresses the limitations of traditional methods in complex marine environments.
  • The developed technique provides a significant advancement for accurately grasping communication system parameters in underwater military applications.