Electromagnetic signal modulation recognition technology based on lightweight deep neural network

Sicheng ZHANG; Yun LIN; Ya TU; Shiwen Mao

doi:10.11959/j.issn.1000-436x.2020237

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Electromagnetic signal modulation recognition technology based on lightweight deep neural network

Topics:Key Technologies of Intelligent Wireless Communication for 6G | 更新时间：2024-06-05

- Electromagnetic signal modulation recognition technology based on lightweight deep neural network
- Journal on Communications Vol. 41, Issue 11, Pages: 12-21(2020)
- 作者机构：
  
  1. 哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
  2. 奥本大学电子和计算机工程学院，奥本 36849
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61771154);The Fundamental Research Funds for the Central Universities(3072020CF0813)
- DOI：10.11959/j.issn.1000-436x.2020237
  CLC： TN971
- Online First：2020-11，
  
  Published：25 November 2020
- 稿件说明：
移动端阅览
Sicheng ZHANG, Yun LIN, Ya TU, et al. Electromagnetic signal modulation recognition technology based on lightweight deep neural network[J]. Journal on Communications, 2020, 41(11): 12-21.
DOI：

Sicheng ZHANG, Yun LIN, Ya TU, et al. Electromagnetic signal modulation recognition technology based on lightweight deep neural network[J]. Journal on Communications, 2020, 41(11): 12-21. DOI： 10.11959/j.issn.1000-436x.2020237.

摘要

针对6G时代将会是移动通信与人工智能紧密结合的时代，产生数量庞大的边缘智能信号处理节点的趋势，提出了一种可部署于资源受限的边缘设备上的高效智能电磁信号识别模型。首先，通过绘制电磁信号的星座图将电磁信号具象为二维图像，并根据归一化点密度对星座图上色以实现特征增强；然后，使用二值化深度神经网络对其进行识别，在保证识别准确率的同时明显降低了模型存储开销以及计算开销。采用电磁信号调制识别

问题进行验证，实验选取常用的8种数字调制信号，选择加性高斯白噪声为信道环境。实验结果表明，所提方案可以在信噪比为-6～6 dB的噪声条件下获得96.1%的综合识别率，网络模型大小仅为166 KB，部署于树莓派4B的执行时间为290 ms，相比于同规模的全精度网络，准确率提升了0.6%，模型缩减到

，运行时间缩减到

。

Abstract

In response to the trend that in the 6th generation wireless (6G) era

mobile communications and artificial intelligence will be closely integrated

and a huge number of edge intelligent signal processing nodes will be deployed

an efficient and intelligent electromagnetic signal recognition model was proposed

which could be deployed on resource-constrained edge devices.The constellation diagram of electromagnetic signal was firstly drawn to visualize electromagnetic signal as a two-dimensional image

and color the constellation diagram according to the normalized point density to achieve feature enhancement.Then

a binary deep neural network was adopted to recognize the colored constellation diagrams.It was shown that the approach can guarantee a high recognition accuracy

which significantly reduced the model storage and calculation costs.For verification

the proposed approach was applied to the problem of electromagnetic signal modulation recognition.The experiment uses eight commonly used digital modulation signals and selects additive white Gaussian noise as the channel environment.The experimental results show that the scheme can achieve a comprehensive recognition rate of 96.1% under the noise condition of -6～6 dB

while the size of the network model is only 166 KB.Further

the execution time

when executed on a Raspberry Pi 4B

is only 290 ms.Compared to a full-precision network of the same scale

the accuracy is increased by 0.6%

the model is reduced to

and the running time is reduced to

关键词

Keywords

references

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