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
Kadnet: Low snr automatic modulation classification via snr aware deformable convolu- tion and kolmogorov–arnold networks,
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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