Dual Loop Meta-Learning for Limited-Sample Modulation Recognition: A Collaborative Model-Agnostic Strategy

This is the authors' abstract. We don't add key points for this paper.

Modulation recognition in electronic support measures systems is challenging due to the scarcity of labeled data. To address this, we propose a Dual-Loop Meta-Learning network for automatic modulation recognition. The network framework consists of an inner loop, where a novel loss function inspired by metric learning optimizes the feature space for task adaptation with limited data, and an outer loop, where a co-learner regularization module mitigates overfitting and optimizes metainitialization parameters. Importantly, the co-learner operates only during meta-training, ensuring no overhead during meta-testing. Experimental results indicate that our proposed algorithm outperforms state-of-the-art few-shot learning methods.