DropBlock based bimodal hybrid neural network for wireless communication modulation recognition

Yan GAO; Jian SHI; Shengyu MA; Bolin MA; Guangxue YUE

doi:10.11959/j.issn.1000-0801.2022099

您当前的位置：

首页 >

文章列表页 >

DropBlock based bimodal hybrid neural network for wireless communication modulation recognition

Research and Development | 更新时间：2024-06-05

- DropBlock based bimodal hybrid neural network for wireless communication modulation recognition
- Telecommunications Science Vol. 38, Issue 5, Pages: 75-86(2022)
- 作者机构：
  
  1. 河南理工大学计算机科学与技术学院，河南焦作 454003
  2. 嘉兴学院信息科学与工程学院，浙江嘉兴 314000
  3. 嘉兴学院数据科学学院，浙江嘉兴 314000
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022099
  CLC： TP393
- Published Online：2022-05，
  
  Published：20 May 2022
- 稿件说明：
移动端阅览
Yan GAO, Jian SHI, Shengyu MA, et al. DropBlock based bimodal hybrid neural network for wireless communication modulation recognition[J]. Telecommunications science, 2022, 38(5): 75-86.
DOI：

Yan GAO, Jian SHI, Shengyu MA, et al. DropBlock based bimodal hybrid neural network for wireless communication modulation recognition[J]. Telecommunications science, 2022, 38(5): 75-86. DOI： 10.11959/j.issn.1000-0801.2022099.

摘要

自动调制识别作为信号检测和解调的中间步骤，在无线通信系统中起着至关重要的作用。针对现有自动调制识别方法识别精度低的问题，提出了一种双模态混合神经网络（bimodal hybrid neural network， BHNN），该网络利用多个模态中包含的互补增益信息来丰富特征维度。将改进的残差网络与双向门控循环单元并行连接，构建双模态混合神经网络模型，分别提取信号的空间特征与时序特征。引入DropBlock正则化算法，有效抑制网络训练过程中过拟合、梯度消失和梯度爆炸等对识别精度的影响。以双模态数据输入，充分利用信号的空间与时序特征，通过并行连接减少网络深度，加速模型收敛，提高调制信号的识别精度。为验证模型的有效性，采用两种公开数据集对模型进行仿真实验，结果表明，BHNN在两种数据集上识别精度高、稳定性强，在高信噪比下识别精度分别可达89%和93.63%。

Abstract

As an intermediate step of signal detection and demodulation

automatic modulation recognition played a momentous role in wireless communication system.Aiming at the low recognition accuracy of existing automatic modulation recognition methods

a bimodal hybrid neural network (BHNN) was proposed

which utilized complementary gain information contained in multiple modes to enrich feature dimensions.The improved residual network was connected in parallel with the bidirectional gated loop unit to construct a bimodal hybrid neural network model

and the spatial and temporal features of the signal were extracted respectively.The DropBlock regularization algorithm was introduced to effectively suppress the influence of over fitting

gradient disappearance and gradient explosion on the recognition accuracy in the process of network training.Using bimodal data input

the spatial and temporal characteristics of signals were fully utilized

and the network depth was reduced through parallel connection.The model convergence was accelerated

and the recognition accuracy of modulated signals was improved.In order to verify the effectiveness of the model

two public datasets were used to simulate the model.The results show that BHNN has high recognition accuracy and strong stability on the two datasets

and the recognition accuracy can reach 89% and 93.63% respectively under high signal-to-noise ratio.

关键词

Keywords

references

WOUTERS J , PATRINOS P , KLOOSTERMAN F , et al . Multi-pattern recognition through maximization of signal-to-peak-interference ratio with application to neural spike sorting [J ] . IEEE Transactions on Signal Processing , 2020 ( 68 ): 6240 - 6254 .

LIU F , ZHOU Y , LIU Y A . A deep neural network method for automatic modulation recognition in OFDM with index modulation [C ] // Proceedings of 2019 IEEE 89th Vehicular Technology Conference . Piscataway:IEEE Press , 2019 : 1 - 5 .

WANG Y , LIU M , YANG J , et al . Data-driven deep learning for automatic modulation recognition in cognitive radios [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 4 ): 4074 - 4077 .

TUNZE G B , HUYNH-THE T , LEE J M , et al . Sparsely connected CNN for efficient automatic modulation recognition [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 12 ): 15557 - 15568 .

LIN C , YAN W J , ZHANG L M , et al . A real-time modulation recognition system based on software-defined radio and multi-skip residual neural network [J ] . IEEE Access , 2020 ( 8 ): 221235 - 221245 .

CHEN S Y , ZHANG Y , HE Z W , et al . A novel attention cooperative framework for automatic modulation recognition [J ] . IEEE Access , 2020 ( 8 ): 15673 - 15686 .

PIJACKOVA K , GOTTHANS T . Radio modulation classification using deep learning architectures [C ] // Proceedings of 2021 31st International Conference Radioelektronika (RADIOELEKTRONIKA) . Piscataway:IEEE Press , 2021 : 1 - 5 .

ZHAO X D , ZHOU X H , XIONG J , et al . Automatic modulation recognition based on multi-dimensional feature extraction [C ] // Proceedings of 2020 International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2020 : 823 - 828 .

赵雄文 , 郭春霞 , 李景春 . 基于高阶累积量和循环谱的信号调制方式混合识别算法 [J ] . 电子与信息学报 , 2016 , 38 ( 3 ): 674 - 680 .

ZHAO X W , GUO C X , LI J C . Mixed recognition algorithm for signal modulation schemes by high-order cumulants and cyclic spectrum [J ] . Journal of Electronics ＆ Information Technology , 2016 , 38 ( 3 ): 674 - 680 .

孙姝君 , 彭盛亮 , 姚育东 , 等 . 基于深度学习的调制识别综述 [J ] . 电信科学 , 2021 , 37 ( 5 ): 82 - 90 .

SUN S J , PENG S L , YAO Y D , et al . A survey of deep learning based modulation recognition [J ] . Telecommunications Science , 2021 , 37 ( 5 ): 82 - 90 .

O’SHEA T J , CORGAN J , CLANCY T C . Convolutional radio modulation recognition networks [M ] // Engineering Applications of Neural Networks . Cham : Springer International Publishing , 2016 : 213 - 226 .

O’SHEA T J , ROY T , CLANCY T C . Over-the-air deep learning based radio signal classification [J ] . IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 168 - 179 .

GHIASI G , LIN T Y , QUOC V . DropBlock:a regularization method for convolutional networks [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18) . New York:ACM Press , 2018 : 10750 - 10760 .

O’SHEA T J , WEST N . Radio machine learning dataset generation with gnu radio [C ] // Proceedings of the GNU Radio Conference . 2016 : 1 - 6 .

CHO K , MERRIENBOER B V , GULCEHRE C , et al . Learning phrase representations using RNN encoder-decoder for statistical machine translation [J ] . Computer Science,arXiv:1406.1078v3 , 2014 .

QI P H , ZHOU X Y , ZHENG S L , et al . Automatic modulation classification based on deep residual networks with multimodal information [J ] . IEEE Transactions on Cognitive Communications and Networking , 2021 , 7 ( 1 ): 21 - 33 .

RAMJEE S , JU S , YANG D , et al . Fast deep learning for automatic modulation classification [J ] . arXiv:1901.05850 , 2019 .

YANG H G , ZHAO L Z , YUE G X , et al . IRLNet:a short-time and robust architecture for automatic modulation recognition [J ] . IEEE Access , 2021 , 9 ( 10 ): 143661 - 143676 .

SRIVASTAVA N , HINTON G , KRIZHEVSKY A , et al . Dropout:a simple way to prevent neural networks from overfitting [J ] . Journal of Machine Learning Research , 2014 , 15 ( 1 ): 1929 - 1958 .

TOMPSON J , GOROSHIN R , JAIN A , et al . Efficient object localization using Convolutional Networks [C ] // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2015 : 648 - 656 .

LU M , PENG T J , YUE G X , et al . Dual-channel hybrid neural network for modulation recognition [J ] . IEEE Access , 2021 ( 9 ): 76260 - 76269 .

Views

310

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Mixed signal modulation recognition method based on temporal depth residual shrinkage network

Modulation recognition driven by signal enhancement

A survey of deep learning based modulation recognition

Related Author

LIU Jinghua

WEI Xianglin

FAN Jianhua

HU Yongyang

WANG Xiaobo

YU Bing

CHENG Fengyun

ZHOU Jin

Related Institution

School of Electronic and Information Engineering, Nanjing University of Information Science and Technology

The 63rd Research Institute, National University of Defense Technology

School of Information Science and Technology, Tianjin University of Finance and Economy

College of Information Science and Engineering, Huaqiao University

Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken NJ

⁰