Mod-CL uses intra-instance modulation consistency to form positive pairs from temporal signal segments in a tailored contrastive objective, outperforming baselines on RadioML datasets especially in low-label regimes.
Sigda: A superimposed domain adaptation framework for automatic modulation classification
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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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Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
Mod-CL uses intra-instance modulation consistency to form positive pairs from temporal signal segments in a tailored contrastive objective, outperforming baselines on RadioML datasets especially in low-label regimes.
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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.