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A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication.

Mengtao Wang1, Youchen Fan1, Shengliang Fang1

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

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|September 9, 2022
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Summary
This summary is machine-generated.

This study introduces a joint deep learning (DL) and expert feature model for automatic modulation discrimination (AMC). The novel approach enhances the distinction between similar Quadrature Amplitude Modulation (QAM) signals, improving classification accuracy.

Keywords:
automatic modulation classificationconvolutional neural networksdeep learningexpert feature methodsspatial cognitive communication

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Automatic modulation discrimination (AMC) is crucial for intelligent receivers in cognitive communication systems.
  • Deep learning (DL) models face challenges in distinguishing between similar modulation schemes like 16QAM and 64QAM due to intra-class diversity.

Purpose of the Study:

  • To propose a novel joint AMC model that combines DL with expert features.
  • To overcome the limitations of existing DL-based AMC methods in differentiating similar QAM signals.

Main Methods:

  • A DL network was developed to extract time-series and phase features from in-phase and quadrature (IQ) samples.
  • Expert features were constructed to enhance the accurate classification of QAM signals.
  • The proposed joint model integrates both DL-extracted and expert-defined features.

Main Results:

  • The joint AMC model demonstrated superior performance compared to benchmark networks.
  • Classification accuracy increased by 11.5% at a 10 dB signal-to-noise ratio (SNR).
  • Improved discrimination capabilities for similar QAM signals were achieved.

Conclusions:

  • The proposed joint AMC model effectively addresses the intra-class diversity challenge in QAM signal discrimination.
  • Combining DL and expert features offers a promising approach for high-performance AMC in intelligent receivers.