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A Lightweight Dual-Branch Complex-Valued Neural Network for Automatic Modulation Classification of Communication

Zhaojing Xu1, Youchen Fan1, Shengliang Fang1

  • 1School of Space Information, Space Engineering University, Beijing 101416, China.

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|April 26, 2025
PubMed
Summary
This summary is machine-generated.

A new lightweight dual-branch complex-valued neural network (LDCVNN) significantly advances automatic modulation classification (AMC). This efficient model achieves high accuracy with minimal parameters, overcoming deployment challenges in signal processing.

Keywords:
Riemannian manifoldautomatic modulation classification (AMC)complex-valued neural networks (CVNNs)deep learning

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Deep learning, particularly complex-valued neural networks (CVNNs), is pivotal for automatic modulation classification (AMC) due to its feature extraction capabilities.
  • CVNNs offer advantages in processing complex communication signals, capturing both amplitude and phase information.
  • Existing CVNN models for AMC suffer from high parameter counts and computational complexity, hindering practical deployment.

Purpose of the Study:

  • To introduce a novel lightweight dual-branch complex-valued neural network (LDCVNN) for efficient and accurate AMC.
  • To address the limitations of existing models regarding parameter count and computational load.
  • To enhance feature extraction and classification performance in complex communication signal processing.

Main Methods:

  • Proposed a dual-branch architecture to separately process phase information and complex-scaling-equivariant representations.
  • Utilized trainable weighted fusion to adaptively combine features from both branches.
  • Extended spatial and channel reconstruction convolution (SCConv) to the complex domain, incorporating complex-valued depthwise separable convolution blocks (CBlock) and average pooling for feature optimization.

Main Results:

  • The LDCVNN achieved the highest average accuracy on the RML2016.10a dataset with only 9.0 K parameters and no data augmentation.
  • Demonstrated significant parameter reduction: 99.33% compared to CDSN and 97.25% compared to CSDNN.
  • Showcased a superior balance between efficiency and performance across multiple datasets.

Conclusions:

  • The LDCVNN offers a highly efficient and effective solution for automatic modulation classification.
  • This model significantly reduces computational complexity and parameter count, making it suitable for deployment in resource-constrained environments.
  • The proposed architecture advances the state-of-the-art in signal processing for wireless communications.