基于BYOL与实复域融合的动态噪声自动调制识别

赵少坤; 贾勇; 张伟; 姚光乐; 张建

doi:10.11959/j.issn.1000-0801.DXKX250549

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基于BYOL与实复域融合的动态噪声自动调制识别

研究与开发 | 更新时间：2026-06-17

- 基于BYOL与实复域融合的动态噪声自动调制识别
- Automatic modulation classification in dynamic noise based on BYOL and real complex domain fusion
- 电信科学 2026年42卷第5期页码：60-73
- 作者机构：
  
  1.成都理工大学机电工程学院，四川成都 610059
  2.电子科技大学长三角研究院（衢州），浙江衢州 324000
  3.电子科技大学通信与信息工程学院，四川成都 611731
  4.电磁空间安全全国重点实验室，四川成都 610036
- 作者简介：
  
  赵少坤（2001- ），男，成都理工大学机电工程学院硕士生，主要研究方向为辐射源个体识别。
  贾勇（1985- ），男，博士，成都理工大学机电工程学院教授、硕士生导师，主要研究方向为智能信号处理、电磁感知。
  张伟（1985- ），男，博士，电子科技大学通信与信息工程学院高级工程师，主要研究方向为电磁感知、电磁大数据。
  姚光乐（1985- ），男，博士，成都理工大学机电工程学院副教授、硕士生导师，主要研究方向为电磁感知、计算机视觉。
  张建（2001- ），男，成都理工大学机电工程学院硕士生，主要研究方向为辐射源个体识别。
- 基金信息：
  
  国家自然科学基金资助项目(U20B2070)
- DOI：10.11959/j.issn.1000-0801.DXKX250549
  中图分类号： TN92
- 收稿：2025-09-12，
  
  修回：2025-11-09，
  
  录用：2025-11-28，
  
  纸质出版：2026-05-20
- 稿件说明：
移动端阅览
赵少坤,贾勇,张伟等.基于BYOL与实复域融合的动态噪声自动调制识别[J].电信科学,2026,42(05):60-73.

Zhao Shaokun,Jia Yong,Zhang Wei,et al.Automatic modulation classification in dynamic noise based on BYOL and real complex domain fusion[J].Telecommunications Science,2026,42(05):60-73.
赵少坤,贾勇,张伟等.基于BYOL与实复域融合的动态噪声自动调制识别[J].电信科学,2026,42(05):60-73. DOI： 10.11959/j.issn.1000-0801.DXKX250549.

Zhao Shaokun,Jia Yong,Zhang Wei,et al.Automatic modulation classification in dynamic noise based on BYOL and real complex domain fusion[J].Telecommunications Science,2026,42(05):60-73. DOI： 10.11959/j.issn.1000-0801.DXKX250549.

摘要

在复杂电磁对抗场景中，无线信道的时变噪声干扰和多域耦合效应使测试数据集的噪声分布特性与训练集的先验假设条件发生偏离，导致深度神经网络模型出现特征失配，进而引发基于静态信道假设的调制识别系统在跨域场景中的性能劣化。为应对这一挑战，提出了一种基于对比学习与实复域融合的动态噪声自动调制识别方法。在预训练阶段，借助BYOL（bootstrap your own latent）对比学习框架，构建实复域融合网络，强制模型通过自监督学习来深刻理解数据的内在结构，从而降低特征提取对噪声分布变化的敏感性，增强模型在不同信噪比条件下的泛化能力；在微调阶段，将短时傅里叶生成的复数时频谱输入实复域融合网络，提取信号的多维特征，使网络学习到与信道噪声无关的本质特征。综合以上两种策略，使模型能够有效地应对不同信噪比条件下的动态噪声干扰。实验结果表明，在待识别信号信噪比下降6 dB的情况下，所提方法相较于现有视觉变换器（vision transformer，ViT）等方法，识别准确率至少提升了23.98%。

Abstract

In complex electromagnetic countermeasure scenarios

the time-varying noise interference of wireless channels and multi-domain coupling effects cause the noise distribution characteristics of the test data set to deviate from the prior assumption conditions of the training set

leading to feature mismatch in deep neural network models and subsequently causing performance degradation of modulation recognition systems based on the static channel assumptions in the cross-domain scenarios. To address this challenge

a dynamic noise automatic modulation recognition method based on contrastive learning and real-complex domain fusion was proposed. In the pre-training stage

the bootstrap your own latent (BYOL) contrastive learning framework was utilized to construct a real-complex domain fusion network

forcing the model to deeply understand the intrinsic structure of the data through self-supervised learning

thereby reducing the sensitivity of feature extraction to the changes in noise distribution and enhancing the model

s generalization ability under different signal-to-noise ratio conditions. In the fine-tuning stage

the complex time-frequency spectrum generated by short-time Fourier transform was input into the real-complex domain fusion network to extract the multi-dimensional features of signal

enabling the network to learn the essential features that independent of channel noise. The combination of these two strategies enabled the model to effectively cope with the dynamic noise interference under different signal-to-noise ratio conditions. Experimental results show that when the signal-to-noise ratio of the signal to be recognized decreases by 6 dB

the proposed method achieves at least a 23.98% improvement in recognition accuracy compared with the existing methods

such as vision Transformer (ViT).

关键词

Keywords

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