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.
A hybrid approach for cross-dataset modulation recognition of wireless inter- ference
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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.