Unsupervised Equivalent Contrastive Learning for Radio Signal Recognition

TL;DR

This paper introduces an unsupervised contrastive learning approach for radio signal recognition that uses multi-domain transformations to create robust, transferable embeddings without labeled data, improving performance across various tasks.

Contribution

The authors propose a novel unsupervised contrastive learning method leveraging four information-lossless transformations across multiple domains to enhance radio signal recognition.

Findings

Outperforms state-of-the-art contrastive methods in various evaluation settings.

Yields significant improvements in few-shot learning and challenging channel conditions.

Demonstrates robustness and generalization through extensive experiments on public datasets.

Abstract

Robust radio signal recognition is fundamental to spectrum management, electromagnetic space security, and intelligent wireless applications, yet existing deep-learning methods rely heavily on large labeled datasets and struggle to capture the multi-domain characteristics inherent in real-world signals. To address these limitations, we propose an unsupervised equivalent contrastive learning method that leverages four information-lossless equivalent transformations, spanning the time, instantaneous, frequency, and time-frequency domains, to construct multi-view and semantically consistent representations of each signal. An equivalent contrastive learning strategy then aligns these complementary views to learn discriminative and transferable embeddings without requiring labeled data. Once pre-training is completed, the resulting model can be directly fine-tuned on downstream tasks using only raw signal samples, without reapplying any equivalent transformations, which reduces computational overhead and simplifies deployment. Extensive experiments on four public datasets demonstrate that the proposed method consistently outperforms state-of-the-art contrastive baselines under linear evaluation, few-shot semi-supervised learning, and cross-domain transfer settings. Notably, the learned representations yield substantial gains in few-shot regimes and challenging channel conditions, confirming the effectiveness of multi-domain equivalent modeling in enhancing robustness and generalization. This work establishes a principled pathway for exploiting massive unlabeled radio data and provides a foundation for future self-supervised learning frameworks in wireless systems.