Recognition of Signal Modulation Pattern Based on Multi-task Self-supervised Learning

Dianjing Cheng, Xingyu Wu, Zhenghao Xie, Zhihua Cui, Qiong Li, Endong Tong, Wenjia Niu, Ziyi Wei, Xinyi Zhao

Abstract: In wireless communication, the recognition of signal modulation plays an essential role. However, acquiring high-quality data in wireless communications is often prohibitively expensive and challenging. Traditional methods for modulation pattern recognition are limited by specialized knowledge, resulting in poor adaptability and generalization. Although deep neural networks demonstrate superior performance in modulation pattern recognition, they heavily depend on high-quality and accurately annotated training data. They require significant computational resources during training, rendering them unsuitable for resource-constrained devices or real-time applications. We propose a signal modulation pattern recognition method based on multi-task self-supervised learning to overcome these challenges. This approach begins by enhancing data from various unlabeled categories, then capturing the essential signal characteristics through contrastive learning to obtain a robust pre-trained model. We then fine-tune the model with a small account of labeled modulation samples to better adapt it to downstream tasks. Experimental results indicate that in scenarios with limited sample availability, our method slightly surpasses traditional recognition methods in accuracy and shows significant advantages in training efficiency.