Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition

• URL: http://arxiv.org/abs/2605.26600v1
• Date: Tue, 26 May 2026 06:33:57 GMT
• Title: Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
• Abstract summary: Dynamic-Adaptive Contrastive Learning (DyCo-CL) is a geometry-aware framework that couples Virtual Adrialversa Augmentation (VAA) with a semantic consistency loss.<n>DyCo-CL achieves a 6.27% accuracy gain in 1-shot settings over prior methods.
• Score: 5.310289270050359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard Self-Supervised Learning (SSL) for Automatic Modulation Recognition (AMR) struggles with ineffective isotropic augmentations, spectral instability, and semantic drift. To address these challenges, we propose Dynamic-Consistency Contrastive Learning (DyCo-CL), a geometry-aware framework that couples Virtual Adversarial Augmentation (VAA) with a semantic consistency loss. We provide a theoretical analysis indicating that this strategy acts as an implicit spectral regularizer for the encoder, enabling stable manifold exploration. Complementing this, our Signal-Adaptive Swin Backbone with fixed-window attention improves structural stability by constraining attention locality, while a Hybrid Knowledge Fusion module anchors representations with physical priors. Experiments on RML benchmarks show that DyCo-CL achieves a 6.27% accuracy gain in 1-shot settings over prior methods.

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