A hybrid CNN+MC-Dropout+BiLSTM system recognizes 14 RF modulation types with 92.6% accuracy by routing high-uncertainty samples to a temporal model.
A survey of modulation classification using deep learning: Signal representation and data preprocessing,
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An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition
A hybrid CNN+MC-Dropout+BiLSTM system recognizes 14 RF modulation types with 92.6% accuracy by routing high-uncertainty samples to a temporal model.