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.
Automatic modulation classification: A deep learning enabled approach
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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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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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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.