A Few-Shot Modulation Recognition Method Based on Pseudo-Label Semi-Supervised Learning

一种基于伪标签半监督学习的小样本调制识别算法

Institute of Information & Navigation, Air Force Engineering University, Xi'an 710077, China

Received: 31 December 2019

Abstract

In order to solve the problem of insufficient labeled samples in modulation recognition, this paper proposes a few-shot modulation recognition algorithm based on pseudo-label semi-supervised learning (pseudo-label algorithm). First of all, high quality artificial feature, excellent classifier and data-labeling method are used to build efficient pseudo label system, and then the pseudo label system is combined with signal classification method based on the deep learning to realize the modulation classification under the condition of a small number of labeled samples and a large number of unlabeled samples. The simulation results show that the pseudo-label algorithm can improve the model recognition performance by 5%-10% when the six kinds of digital signals are classified and identified and its SNR is greater than 5 dB. At the same time, the algorithm has a simple network design and is of great application value.

摘要

针对有标签样本较少条件下的通信信号调制识别问题，提出了一种基于伪标签半监督学习技术的小样本调制方式分类算法，通过优选人工特征集、设计高性能分类器以及基于输出概率的伪标签数据选择方法，构建高效的伪标签标注系统，然后通过该伪标签标注系统与基于深度学习的信号分类方法配合，实现在少量有标签样本和大量无标签样本条件下的调制方式分类。仿真结果表明，对6种数字信号进行调制识别，在信噪比大于5 dB时，伪标签算法可将模型识别性能提高5%~10%，该算法设计简单，具有较大应用价值。