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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.
Sensors (Basel, Switzerland)
|August 14, 2025
Summary
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.
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.
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