A Semi-Supervised Signal Modulation Mode Recognition Algorithm Based on Deep Learning

被引：0
|
作者：
Zhang, Bolin [1 ]
Ji, Gang [2 ]
Zhu, Yuxuan [2 ]
Xu, Xiangnan [3 ]
Tang, Wanbin [3 ]
机构：
[1] School of System Science and Engineering, Sun Yat‑sen University, Guangzhou
[2] Institute of Systems Engineering, Academy of Military Science of the People’s Liberation Army, Beijing
[3] National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2024年 / 53卷 / 04期
关键词：
convolutional neural network; modulation; semi‑supervised learning; signal recognition;
D O I：
10.12178/1001-0548.2022252
中图分类号：
学科分类号：
摘要：
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. © 2024 University of Electronic Science and Technology of China. All rights reserved.
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收藏
页码：511 / 518
页数：7
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