[1]江　桦,肖科杰,胡　坡,等.基于BLR并行结构的多模态调制识别方法[J].郑州大学学报(工学版),2026,47(3):76-82,116.[doi:10.13705/j.issn.1671-6833.2025.03.019]
　JIANG Hua,XIAO Kejie,HU Po,et al.Multimodal Modulation Recognition Method Based on BLR Parallel Structure[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):76-82,116.[doi:10.13705/j.issn.1671-6833.2025.03.019]
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基于BLR并行结构的多模态调制识别方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
76-82,116
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
Multimodal Modulation Recognition Method Based on BLR Parallel Structure
文章编号:
1671-6833(2026)03-0076-07
作者:
江　桦, 肖科杰, 胡　坡, 巩克现, 赵振禹
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
JIANG Hua, XIAO Kejie, HU Po, GONG Kexian, ZHAO Zhenyu
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
自动调制识别;  卷积神经网络;  多模态;  特征融合;  并联结构
Keywords:
automatic modulation recognition;  convolutional neural network;  multimodal;  feature fusion;  parallel structure
分类号:
TN911.7
DOI:
10.13705/j.issn.1671-6833.2025.03.019
文献标志码:
A
摘要:
针对现有基于卷积神经网络(CNN)的调制识别方法对单一模态数据(如IQ序列)依赖性强、难以充分提取信号多维特征等问题,提出了一种基于双向长短时记忆网络(BiLSTM)和残差网络(ResNet)的多模态并行结构调制识别方法(BLR网络)。首先,通过上支路的BiLSTM提取IQ数据的时序特征,通过下支路的ResNet-18提取星座图的空间特征;其次,在决策融合模块采用串行特征融合,更好地挖掘多模态数据的互补性;最后,借助模型的特征提取能力对信号调制样式进行识别,并在公开数据集RML2018.01a上进行了实验验证。实验结果表明:BLR网络在6~30 dB信噪比区间内的整体识别准确率稳定在96.48%,相较于单一模态的ResNet和BiLSTM模型分别提升了2.61%和3.91%,相较于并联结构的CNN-LSTM模型提高了1.25%,验证了所提模型在调制识别问题上的有效性。
Abstract:
Aiming at the problem that existing convolutional neural network (CNN)-based modulation recognition methods are highly dependent on single modal data (e.g., IQ sequences) and difficult to adequately extract multidimensional features of signals, in this study a multimodal parallel structural modulation recognition method was proposed based on bidirectional long short-term memory network (BiLSTM) and residual network (ResNet), termed the BiLSTM-ResNet (BLR network). Firstly, the temporal features of IQ data were extracted by BiLSTM in the upper branch, and the spatial features of constellation maps were extracted by ResNet-18 in the lower branch. Secondly, serial feature fusion was used in the decision fusion module to better exploit the complementary nature of the multimodal data. Lastly, the signal modulation styles were recognised with the help of the model’s feature extraction capability. In this study, experimental validation was carried out on the publicly available dataset RML2018.01a. The experimental results showed that the overall recognition accuracy of BLR network in the 6-30 dB SNR interval was stable at 96.48%, 2.61% and 3.91% higher than that of the single-modal ResNet and BiLSTM models, respectively, and 1.25% higher than that of the CNN-LSTM model with concatenated structure, which verified that the model proposed in this paper had the modulation recognition problem Effectiveness.

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更新日期/Last Update: 2026-05-27