Dualformer applies a parameter-sharing DualNN via Transformer patches to complex signals, claiming better results on AMR, SSR, and SSP tasks than baselines.
Deep neural network architectures for modulation classification
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 2representative citing papers
GAMC is a four-stage interpretable ML pipeline for AMC that transforms I/Q signals into constellation and graph representations, extracts features, learns discriminative projections, and uses SNR soft routing to achieve higher accuracy with 50% fewer parameters and 3-42% of the compute of comparable
citing papers explorer
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Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis
Dualformer applies a parameter-sharing DualNN via Transformer patches to complex signals, claiming better results on AMR, SSR, and SSP tasks than baselines.
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Automatic Modulation Classification via Green Machine Learning
GAMC is a four-stage interpretable ML pipeline for AMC that transforms I/Q signals into constellation and graph representations, extracts features, learns discriminative projections, and uses SNR soft routing to achieve higher accuracy with 50% fewer parameters and 3-42% of the compute of comparable