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.
Cite
@article{arxiv.2605.26600,
title = {Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition},
author = {Guanqun Zhao and Yitong Liu and Jiaxuan Fang and Yufei Mao and Hongwen Yang},
journal= {arXiv preprint arXiv:2605.26600},
year = {2026}
}