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The aim is to review deep learning techniques for automatic modulation classification (AMC) and their effectiveness in diverse conditions.

May 20, 2026ElectronicsOpen Access

Deep Learning for Automatic Modulation Classification: A Review

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Review analyzes deep learning techniques for automatic modulation classification, suggesting improvements for real-world applications.

The aim is to review deep learning techniques for automatic modulation classification (AMC) and their effectiveness in diverse conditions.
Structured review of deep learning models, including CNNs, RNN/LSTM, and transformer architectures.
Analysis of representation choices, dataset design, and evaluation protocols impacting performance.
Identification of challenges such as domain shift and low-SNR environments.
Deep learning models demonstrate improved adaptability compared to traditional methods in diverse signal conditions.
Key challenges include effective handling of multi-signal interference and ensuring robustness in low-SNR environments.
Future directions emphasize integrating classical signal processing with learning-based approaches.

Thakur et al. (2026) studied this question.

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