MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition

TL;DR

This paper introduces MCLRL, a framework combining contrastive and reinforcement learning to improve few-shot modulation recognition in wireless communications, addressing data scarcity and enhancing feature extraction.

Contribution

The paper presents a novel MCLRL framework that integrates multi-domain contrastive learning with reinforcement learning for effective few-shot modulation recognition.

Findings

Achieves high classification accuracy with limited samples.

Effectively extracts deep signal features for FSL tasks.

Maintains flexibility across different signal models.

Abstract

With the rapid advancements in wireless communication technology, automatic modulation recognition (AMR) plays a critical role in ensuring communication security and reliability. However, numerous challenges, including higher performance demands, difficulty in data acquisition under specific scenarios, limited sample size, and low-quality labeled data, hinder its development. Few-shot learning (FSL) offers an effective solution by enabling models to achieve satisfactory performance with only a limited number of labeled samples. While most FSL techniques are applied in the field of computer vision, they are not directly applicable to wireless signal processing. This study does not propose a new FSL-specific signal model but introduces a framework called MCLRL. This framework combines multi-domain contrastive learning with reinforcement learning. Multi-domain representations of signals enhance feature richness, while integrating contrastive learning and reinforcement learning architectures enables the extraction of deep features for classification. In downstream tasks, the model achieves excellent performance using only a few samples and minimal training cycles. Experimental results show that the MCLRL framework effectively extracts key features from signals, performs well in FSL tasks, and maintains flexibility in signal model selection.