基于DropBlock双模态混合神经网络的无线通信调制识别

高岩; 石坚; 马圣雨; 马柏林; 乐光学

doi:10.11959/j.issn.1000-0801.2022099

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基于DropBlock双模态混合神经网络的无线通信调制识别

研究与开发 | 更新时间：2024-06-05

- 基于DropBlock双模态混合神经网络的无线通信调制识别
- DropBlock based bimodal hybrid neural network for wireless communication modulation recognition
- 电信科学 2022年38卷第5期页码：75-86
- 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：
  
  [ "高岩（1963-），男，博士，河南理工大学计算机科学与技术学院教授，主要研究方向为智能信息处理与智能控制等" ]
  [ "石坚（1996- ），男，河南理工大学硕士生，主要研究方向为深度学习与智能通信" ]
  [ "马圣雨（1997- ），女，河南理工大学硕士生，主要研究方向为深度学习与智能通信" ]
  [ "马柏林（1961- ），男，博士，嘉兴学院数据科学学院教授，主要研究方向为调和分析和小波分析、智能计算" ]
  [ "乐光学（1963- ），男，博士，嘉兴学院信息科学与工程学院教授，主要研究方向为多云融合与协同服务、边缘计算与一体化通信网络、深度学习与智能通信" ]
- 基金信息：
  
  国家自然科学基金重点项目;The National Natural Science Foundation Key Project of China(U19B2015)
- DOI：10.11959/j.issn.1000-0801.2022099
  中图分类号： TP393
- 网络出版日期：2022-05，
  
  纸质出版日期：2022-05-20
- 稿件说明：
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高岩, 石坚, 马圣雨, 等. 基于DropBlock双模态混合神经网络的无线通信调制识别[J]. 电信科学, 2022,38(5):75-86.

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.
高岩, 石坚, 马圣雨, 等. 基于DropBlock双模态混合神经网络的无线通信调制识别[J]. 电信科学, 2022,38(5):75-86. DOI： 10.11959/j.issn.1000-0801.2022099.

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.

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references

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