Selecting IQ, amplitude-phase, and autocorrelation representations as complementary signal priors, then fusing them with a lightweight attention mechanism and adversarial domain alignment, improves cross-domain modulation classification by 8–15 percentage points over source-only baselines.
A spatiotemporal multi-channel learning framework for automatic modulation recognition
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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
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DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification
Selecting IQ, amplitude-phase, and autocorrelation representations as complementary signal priors, then fusing them with a lightweight attention mechanism and adversarial domain alignment, improves cross-domain modulation classification by 8–15 percentage points over source-only 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