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An explainable Grey Wolf optimized extreme learning machine framework for modulation classification in cloud

Padma Charan Sahu1, Bibhu Prasad1, Ratnakar Dash2

  • 1Department of Electronics and Communication Engineering, GIET University, Gunupur, Odisha, India.

Scientific Reports
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces XGEM-Net, an explainable model for automatic modulation classification (AMC) in wireless communications. The cloud-based XGEM-Net achieves high accuracy, outperforming existing methods.

Keywords:
Automatic modulation classificationDeep feature fusionExplainable artificial intelligenceExtreme learning machineGrey Wolf optimization

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Area of Science:

  • Wireless Communication Systems
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automatic modulation classification (AMC) is crucial for efficient spectrum use and reliable data transmission in modern wireless systems.
  • Existing AMC models often lack interpretability and optimal performance in cloud environments.

Purpose of the Study:

  • To propose XGEM-Net, a novel, explainable AMC model optimized for cloud-centric environments.
  • To enhance the performance and interpretability of modulation classification.

Main Methods:

  • Feature extraction from pre-trained models (InceptionV3, ResNet-50, MobileNetV2) and concatenation.
  • Optimization of Extreme Learning Machine (ELM) using Grey Wolf Optimization (GWO) for classification.
  • Integration of Local Interpretable Model-Agnostic Explanations (LIME) for model interpretability.

Main Results:

  • The cloud-based XGEM-Net demonstrated superior performance compared to standalone configurations.
  • The vCPU-16 (64 GB RAM) configuration achieved 95.16% accuracy, 90.78% sensitivity, and 89.83% specificity.
  • The proposed model consistently outperformed state-of-the-art methods in classification accuracy.

Conclusions:

  • XGEM-Net offers a highly accurate and interpretable solution for AMC in cloud environments.
  • Cloud-based deployment significantly enhances the performance of the proposed AMC model.
  • The explainable nature of XGEM-Net provides valuable insights into classifier decisions.