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DGrA: Lightweight Modulation Recognition Based on Hybrid Neural Networks.

Xu Chen1, Rui Gao2, Ding Xu1

  • 1Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
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This study introduces a novel lightweight method for automatic modulation recognition, enhancing accuracy and reducing model size. The approach effectively differentiates signal sequences for intelligent transmission applications.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Automatic modulation recognition is vital for non-cooperative communication and intelligent transmission.
  • Existing methods often face challenges with spatial costs and computational complexity.

Purpose of the Study:

  • To propose a new lightweight method for automatic modulation recognition.
  • To enhance recognition accuracy while reducing spatial and computational costs.
  • To validate the method's effectiveness in real-world scenarios.

Main Methods:

  • Combines an improved attention block and convolutional operations with a recurrent neural network.
  • Utilizes depthwise separable convolutions to reduce computational complexity and enhance feature extraction.
Keywords:
SDRdeep learningfeature extractionintelligent signal processinglightweightmodulation recognition

Related Experiment Videos

  • Incorporates pruning to reduce ineffective features and decrease model size.

    • Main Results:

    • The proposed method outperforms comparative methods on the RadioML2016.10a dataset in terms of recognition accuracy and model size.
    • Achieved 87.22% accuracy on a software-defined radio platform under practical conditions with environmental noise.
    • Demonstrated reduced computational complexity and enhanced feature extraction capabilities.

    Conclusions:

    • The developed lightweight method offers superior performance for automatic modulation recognition.
    • The approach is effective in real-world applications, even in noisy environments.
    • This technique contributes to more efficient and accurate intelligent transmission systems.