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.
Online hybrid likelihood based modulation classification us- ing multiple sensors
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SP 2years
2026 2roles
background 1polarities
background 1representative citing papers
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.
citing papers explorer
-
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.
-
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.