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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

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 classification Deep feature fusion Explainable artificial intelligence Extreme learning machine Grey 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.