一种复数域轻量化知识蒸馏驱动的调制识别模型

王子恒; 张徐; 高硕; 周金

doi:10.11959/j.issn.1000-0801.2025173

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一种复数域轻量化知识蒸馏驱动的调制识别模型

研究与开发 | 更新时间：2025-09-04

- 一种复数域轻量化知识蒸馏驱动的调制识别模型
- A modulation recognition model driven by lightweight knowledge distillation in the complex number domain
- 电信科学 2025年41卷第8期页码：163-175
- 作者机构：
  
  天津财经大学理工学院，天津 300222
- 作者简介：
  
  [ "王子恒（2000- ），男，天津财经大学理工学院硕士生，主要研究方向为自动调制识别、图神经网络。" ]
  [ "张徐（1999- ），男，天津财经大学理工学院硕士生，主要研究方向为自动调制识别、多模态融合。" ]
  [ "高硕（2001- ），男，天津财经大学理工学院硕士生，主要研究方向为全局及局部特征协作的识别网络。" ]
  [ "周金（1981- ），女，博士，天津财经大学理工学院计算机与信息工程系系主任、硕士生导师、副教授，主要研究方向为调制识别与频谱感知。" ]
- 基金信息：
  
  天津自然科学基金资助项目(22JCYBJC01550)
- DOI：10.11959/j.issn.1000-0801.2025173
  中图分类号： TP393
- 收稿：2024-12-01，
  
  修回：2025-02-27，
  
  录用：2025-03-19，
  
  纸质出版：2025-08-20
- 稿件说明：
移动端阅览
王子恒,张徐,高硕等.一种复数域轻量化知识蒸馏驱动的调制识别模型[J].电信科学,2025,41(08):163-175.

WANG Ziheng,ZHANG Xu,GAO Shuo,et al.A modulation recognition model driven by lightweight knowledge distillation in the complex number domain[J].Telecommunications Science,2025,41(08):163-175.
王子恒,张徐,高硕等.一种复数域轻量化知识蒸馏驱动的调制识别模型[J].电信科学,2025,41(08):163-175. DOI： 10.11959/j.issn.1000-0801.2025173.

WANG Ziheng,ZHANG Xu,GAO Shuo,et al.A modulation recognition model driven by lightweight knowledge distillation in the complex number domain[J].Telecommunications Science,2025,41(08):163-175. DOI： 10.11959/j.issn.1000-0801.2025173.

摘要

深度学习模型在调制识别任务中依赖大量训练样本，但实际场景中信号样本有限，尤其在复杂噪声环境下，模型性能受限。为此，提出了一种基于局部特征引导的轻量化调制识别方法。首先，构建轻量化教师网络以提取含噪调制信号的局部特征，并设计局部语义特征优化算法将局部知识蒸馏给学生网络；其次，针对调制信号频谱的复数域特性，设计复数域Transformer作为学生网络进行全局特征提取，并最终完成识别任务。实验结果表明，所提模型在小样本场景下相比其他深度学习模型具有更高的识别效率，在计算复杂度和实时性等方面较现有方法表现出明显优势。

Abstract

Deep learning models rely on a large number of training samples in the modulation recognition task. However

in actual scenarios

the signal samples are limited

especially in complex noise environments

where the model performance is restricted. Therefore

a lightweight modulation recognition method based on local feature guidance was proposed. Firstly

a lightweight teacher network was constructed to extract local features from noisy modulated signals

and a local semantic feature optimization algorithm was designed to distill local knowledge into the student network. Secondly

aiming at the complex-domain characteristics of the modulated signal spectrum

a complex-domain Transformer was designed as the student network for global feature extraction

ultimately completing the recognition task. Experimental results show that the proposed model demonstrates higher recognition efficiency in small-sample scenarios compared with other deep learning models

and exhibits significant advantages in terms of computational complexity and real-time performance compared with existing methods.

关键词

Keywords

references

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