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DFENet: A diverse feature extraction neural network for improving automatic modulation classification accuracy in

Ha-Khanh Le¹, Van-Phuc Hoang¹, Van-Sang Doan²

¹Institute of System Integration, Le Quy Don Technical University, Hanoi, Vietnam.

|January 21, 2026

Summary

A new deep learning model, DFENet, enhances automatic modulation classification (AMC) accuracy in wireless communications. It uses diverse feature extraction blocks to improve signal identification, especially at low signal-to-noise ratios (SNRs).

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Area of Science:

Wireless Communications
Machine Learning
Signal Processing

Background:

Automatic Modulation Classification (AMC) is crucial for wireless systems.
Existing methods face challenges with accuracy and computational complexity.

Purpose of the Study:

To introduce DFENet, a novel convolutional neural network (CNN) for improved AMC.
To enhance AMC accuracy using diverse feature extraction (DFE) blocks.

Main Methods:

DFENet employs multi-branch DFE blocks with multi-scale filters.
Extracts signal features from In-phase and Quadrature-phase (IQ) data.
Utilizes convolutional layers with varied filter sizes to prevent overfitting and gradient vanishing.

Main Results:

DFENet achieved 82.76% average AMC accuracy on the HisarMod2019 dataset.
Demonstrated high accuracy at low SNRs (e.g., >60% at -20 dB).
Exceeded 93% accuracy on the RadioML2018.01A dataset for SNR >6 dB.

Conclusions:

DFENet significantly improves AMC accuracy compared to state-of-the-art models.
Maintains reasonable computational complexity and fast execution.
Offers a robust solution for modulation classification in challenging wireless environments.