Modulation recognition network of multi-scale analysis with deep threshold noise elimination

Xiang LI; Yibing LI; Chunrui TANG; Yingsong LI

doi:10.1631/FITEE.2200253

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Modulation recognition network of multi-scale analysis with deep threshold noise elimination

Regular Papers | Updated：2023-05-31

- Modulation recognition network of multi-scale analysis with deep threshold noise elimination
- 具有深度阈值噪声消除的多尺度分析调制识别网络
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 5, Pages: 742-758(2023)
- Affiliations：
  
  1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  2.Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China
  3.China Coal Technology Engineering Group Chongqing Research Institute, Chongqing 400037, China
  4.State Key Lab of Methane Disaster Monitoring & Emergency Technology, Chongqing 400039, China
- Author bio：
  
  ‡ Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200253
  CLC： TN911.3
- Received：10 June 2022，
  
  Accepted：26 August 2022，
  
  Published：0 May 2023
- Accepted：
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Xiang LI, Yibing LI, Chunrui TANG, et al. Modulation recognition network of multi-scale analysis with deep threshold noise elimination[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(5): 742-758.
DOI：

Xiang LI, Yibing LI, Chunrui TANG, et al. Modulation recognition network of multi-scale analysis with deep threshold noise elimination[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(5): 742-758. DOI： 10.1631/FITEE.2200253.

摘要

为了提高多变环境下调制信号识别的准确性、减少先验知识不足等因素对识别结果的影响，研究人员逐渐采用深度学习技术来替代传统的调制信号处理技术。为了解决低信噪比下调制信号识别精度低的问题，我们设计了一种具有深度阈值噪声消除的多尺度分析调制识别网络，在标签平滑的对称交叉熵函数作用下识别实际采集的调制信号。该网络由一个具有深度自适应阈值学习的消噪编码器和一个具有多尺度特征融合的解码器组成。将两个模块进行跳跃连接，共同作用以提高整体网络的鲁棒性。实验结果表明，该方法在低信噪比下比以前的方法具有更好的识别效果。该网络展示了对噪声阈值的灵活自学习能力以及所设计的特征融合模块对各种调制类型的多尺度特征获取的有效性。

Abstract

To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results

researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios

we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion. The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

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