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DualBranch-AMR: A Semi-Supervised AMR Method Based on Dual-Student Consistency Regularization with Dynamic Stability

Jiankun Ma1, Zhenxi Zhang1, Linrun Zhang1

  • 1The Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi'an 710126, China.

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Summary
This summary is machine-generated.

This study introduces a novel semi-supervised method for Automatic Modulation Recognition (AMR) using a dual-student framework. The approach effectively utilizes unlabeled data, achieving high accuracy with minimal labeled data, outperforming traditional methods.

Keywords:
automatic modulation recognitionconsistency constraintsdual-student modeldynamic stabilitysemi-supervised

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Area of Science:

  • Wireless Communications
  • Machine Learning
  • Signal Processing

Background:

  • Deep learning significantly enhances Automatic Modulation Recognition (AMR) but requires extensive labeled data.
  • High annotation costs and privacy concerns necessitate exploring semi-supervised learning for AMR.
  • Leveraging unlabeled data is crucial for efficient and practical AMR system development.

Purpose of the Study:

  • To develop a semi-supervised Automatic Modulation Recognition (AMR) method that effectively utilizes unlabeled data.
  • To improve the accuracy of pseudo-labels generated for unlabeled data through dynamic stability evaluation.
  • To propose a novel stability-guided consistency regularization constraint for semi-supervised AMR training.

Main Methods:

  • A dual-branch co-training architecture is employed to maximize the exploitation of unlabeled data and learn deep feature representations.
  • A dynamic stability evaluation module, utilizing strong and weak augmentation, refines the accuracy of pseudo-labels.
  • A stability-guided consistency regularization constraint is integrated into the dual-student semi-supervised framework for model training.

Main Results:

  • The proposed DualBranch-AMR method demonstrates superior performance compared to supervised baselines on benchmark datasets.
  • With only 5% labeled data, the method achieves 55.84% recognition accuracy.
  • The performance reaches over 90% of fully supervised training, validating its effectiveness under semi-supervised conditions.

Conclusions:

  • The developed semi-supervised AMR method effectively leverages unlabeled data, significantly reducing the need for labeled samples.
  • The dual-student framework combined with stability-guided regularization offers a promising approach for practical AMR systems.
  • This research highlights the potential of semi-supervised learning to overcome data limitations in wireless communication applications.