基于可微分架构搜索的多载波信号自动调制识别

李杰; 李靖; 吕璐; 宫丰奎

doi:10.11959/j.issn.1000-436x.2024164

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基于可微分架构搜索的多载波信号自动调制识别

学术论文 | 更新时间：2024-10-10

- 基于可微分架构搜索的多载波信号自动调制识别
- Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
- 通信学报 2024年45卷第9期页码：14-25
- 作者机构：
  
  西安电子科技大学空天地一体化综合业务网全国重点实验室，陕西西安 710071
- 作者简介：
  
  [ "李杰（2001- ），男，陕西铜川人，西安电子科技大学博士生，主要研究方向为卫星通信信号分析与处理。" ]
  [ "李靖（1980- ），男，湖北荆州人，博士，西安电子科技大学教授，主要研究方向为宽带无线传输、人工智能与无线通信融合。" ]
  [ "吕璐（1990- ），男，河北张家口人，博士，西安电子科技大学副教授，主要研究方向为物理层安全、通信感知一体化。" ]
  [ "宫丰奎（1979- ），男，山东潍坊人，博士，西安电子科技大学教授，主要研究方向为新一代无线通信关键技术。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62271368;62001354);陕西省重点研发计划基金资助项目(2023-YBGY-041);中国博士后科学基金资助项目(BX20190264;2019M650258)
- DOI：10.11959/j.issn.1000-436x.2024164
  中图分类号： TN92
- 收稿：2024-05-10，
  
  修回：2024-08-17，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
李杰,李靖,吕璐等.基于可微分架构搜索的多载波信号自动调制识别[J].通信学报,2024,45(09):14-25.

LI Jie,LI Jing,LYU Lu,et al.Differentiable architecture search-based automatic modulation recognition for multi-carrier signals[J].Journal on Communications,2024,45(09):14-25.
李杰,李靖,吕璐等.基于可微分架构搜索的多载波信号自动调制识别[J].通信学报,2024,45(09):14-25. DOI： 10.11959/j.issn.1000-436x.2024164.

LI Jie,LI Jing,LYU Lu,et al.Differentiable architecture search-based automatic modulation recognition for multi-carrier signals[J].Journal on Communications,2024,45(09):14-25. DOI： 10.11959/j.issn.1000-436x.2024164.

摘要

针对城市多径信道下缺乏多载波信号通用数据集，以及传统信号特征与网络模型难以有效识别低信噪比下失真信号调制类型的问题，提出一种基于可微分架构搜索的多载波信号自动调制识别算法。首先，产生了常见OFDM、FBMC与OTFS多载波信号经过典型城市多径信道的接收信号数据集，选取对调制参数不敏感的信号时频图作为特征向量来训练神经网络；其次，采用可微分架构搜索方法自动搜索最佳网络结构，避免了网络结构设计的反复验证工作；最后，在特征学习过程中引入联合注意力机制，将失真信号特征进行空间转换以降低多径干扰影响，同时计算特征图各通道信息权重并排序，以提升相关特征图通道的分类效果。仿真结果表明，所提算法不仅能提升在城市多径信道环境下尤其是低信噪比时的识别正确率，而且对调制参数变化和小样本场景具有更好的鲁棒性。

Abstract

Considering the lack of a general multi-carrier signal dataset in urban multipath channels

and the challenge of recognizing the modulation types of distorted signals at low signal-to-noise ratio (SNR)

a differentiable architecture search-based (DARTS) automatic modulation recognition algorithm for multi-carrier signals was proposed. Firstly

the received signal datasets of commonly used multi-carrier signals

i.e.

orthogonal frequency division multiplexing

filter bank multi-carrier

and orthogonal time frequency space

were generated over typical urban multipath channels. The time-frequency images

which were insensitive to modulation parameters

were selected as feature vectors to train the neural network. Secondly

DARTS was employed to automatically search the optimal network architecture. Finally

a joint attention mechanism was introduced into the feature learning process. This mechanism spatially transforming distorted signal features to mitigate the impact of multipath interference

while also calculating and sorting the information weights for each channel of the feature maps to improve the recognition performance of the relevant feature map channels. Simulation results demonstrate that the proposed algorithm improves accuracy in urban multipath channels

especially at low SNR

while simultaneously providing better robustness to modulation parameter variations and small-sample scenarios.

关键词

Keywords

references

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