电子科技大学学报2024，Vol.53Issue(4)：511-518,8.DOI:10.12178/1001-0548.2022252
基于深度学习的半监督信号调制样式识别算法
A Semi-Supervised Signal Modulation Mode Recognition Algorithm Based on Deep Learning
摘要
Abstract
Benefiting from the development of deep learning, great progress has been achieved in using neural networks to improve signal recognition performance. However, most of the existing deep learning-based signal recognition methods are supervised, which requires a large amount of well-labeled data for training, but the cost of signal labeling is quite expensive. This encourages the semi-supervised methods to make full use of unlabeled data to assist the training of deep models, but existing semi-supervised signal recognition methods do not consider noise influence. Therefore, a semi-supervised signal recognition method is proposed based on deep residual network (Resnet) by using gradient reversal layers to improve noise effect on performance. Experimental results on open source datasets RML2016.10A, RML2016.10B and RML2016.10C show that the proposed semi-supervised method effectively extracts discriminative features from unlabeled data by using a small amount of labeled data information, which alleviates noise influence.关键词
调制样式/半监督学习/卷积神经网络/信号识别Key words
modulation/semi-supervised learning/convolutional neural network/signal recognition分类
信息技术与安全科学引用本文复制引用
张柏林,姬港,朱宇轩,许向楠,唐万斌..基于深度学习的半监督信号调制样式识别算法[J].电子科技大学学报,2024,53(4):511-518,8.基金项目
新疆维吾尔自治区自然科学基金(2022D01B184) （2022D01B184）
中国博士后科学基金(2020M683290,2021T140095) （2020M683290,2021T140095）
中央高校基本科研业务费(ZYGX2021J031) （ZYGX2021J031）
