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An ultra lightweight neural network for automatic modulation classification in drone communications.

Mengtao Wang1, Shengliang Fang2, Youchen Fan3

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This study introduces an ultra-lightweight neural network (ULNN) for automatic modulation classification (AMC) on unmanned aerial vehicles (UAVs). The ULNN achieves high accuracy with minimal parameters, making it suitable for resource-constrained UAV communication systems.

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Area of Science:

  • Wireless Communication
  • Deep Learning
  • Signal Processing

Background:

  • Unmanned aerial vehicle (UAV)-assisted communication enhances transmission efficiency using automatic modulation classification (AMC).
  • Existing deep learning (DL)-based AMC methods face challenges on UAVs due to limited computing power and storage, creating a trade-off between accuracy and efficiency.
  • Resource-constrained scenarios necessitate lightweight DL models for effective AMC on UAV platforms.

Purpose of the Study:

  • To develop a lightweight DL-based AMC network adaptable to resource-constrained UAV platforms.
  • To address the accuracy-efficiency contradiction in DL-based AMC for UAVs.
  • To propose an ultra-lightweight neural network (ULNN) for improved UAV communication.

Main Methods:

  • Proposed an ultra-lightweight neural network (ULNN) integrating a lightweight convolutional structure, attention mechanism, and cross-channel feature fusion.
  • Introduced data augmentation (DA) using signal phase offsets to enhance model generalization and prevent overfitting.
  • Validated the ULNN on the RML2016.10A dataset.

Main Results:

  • The proposed ULNN achieved an average precision of 62.83% with only 8,815 parameters.
  • A peak classification accuracy of 92.11% was reached at a signal-to-noise ratio (SNR) of 10 dB.
  • Demonstrated high recognition accuracy with a significantly reduced model size.

Conclusions:

  • The ULNN effectively balances high recognition accuracy with a lightweight model architecture.
  • The proposed network is well-suited for deployment on UAV platforms with limited computational resources.
  • This research contributes to advancing efficient wireless communication for UAVs through optimized DL-based AMC.