RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder

TL;DR

RIS-MAE introduces a self-supervised autoencoder framework that learns from raw IQ signals for modulation classification, overcoming the limitations of supervised methods and improving adaptability and generalization in wireless communication tasks.

Contribution

It proposes a novel self-supervised learning approach using masked autoencoders on raw IQ signals, enhancing feature learning and reducing dependence on labeled data.

Findings

Outperforms existing methods in few-shot learning scenarios

Achieves high accuracy on unseen datasets with minimal fine-tuning

Demonstrates strong generalization and real-world applicability

Abstract

Automatic modulation classification (AMC) is a basic technology in intelligent wireless communication systems. It is important for tasks such as spectrum monitoring, cognitive radio, and secure communications. In recent years, deep learning methods have made great progress in AMC. However, mainstream methods still face two key problems. First, they often use time-frequency images instead of raw signals. This causes loss of key modulation features and reduces adaptability to different communication conditions. Second, most methods rely on supervised learning. This needs a large amount of labeled data, which is hard to get in real-world environments. To solve these problems, we propose a self-supervised learning framework called RIS-MAE. RIS-MAE uses masked autoencoders to learn signal features from unlabeled data. It takes raw IQ sequences as input. By applying random masking and reconstruction, it captures important time-domain features such as amplitude, phase, etc. This helps the model learn useful and transferable representations. RIS-MAE is tested on four datasets. The results show that it performs better than existing methods in few-shot and cross-domain tasks. Notably, it achieves high classification accuracy on previously unseen datasets with only a small number of fine-tuning samples, confirming its generalization ability and potential for real-world deployment.