A sparse coding plus hierarchical tree pipeline for automatic modulation classification cuts model parameters by 41% and FLOPs to 10^{-4} of lightweight deep learning baselines.
Genetic algorithm optimized distribution sampling test for m-qam modulation classification,
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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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G-AMC: A Green Automatic Modulation Classification Method
A sparse coding plus hierarchical tree pipeline for automatic modulation classification cuts model parameters by 41% and FLOPs to 10^{-4} of lightweight deep learning 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