基于深度学习的调制识别综述

孙姝君; 彭盛亮; 姚育东; 杨喜

doi:10.11959/j.issn.1000-0801.2021114

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基于深度学习的调制识别综述

专题：通信与AI融合 | 更新时间：2024-06-05

- 基于深度学习的调制识别综述
- A survey of deep learning based modulation recognition
- 电信科学 2021年37卷第5期页码：82-90
- 作者机构：
  
  1. 华侨大学信息科学与工程学院，福建厦门 361021
  2. 史蒂文斯理工学院电子与计算机工程系，美国新泽西州霍博肯 07030
  3. 吉首大学信息科学与工程学院，湖南吉首 416000
- 作者简介：
  
  [ "孙姝君（1997− ），女，华侨大学信息科学与工程学院硕士生，主要研究方向为无线通信、人工智能" ]
  [ "彭盛亮（1982− ），男，博士，华侨大学信息科学与工程学院副教授、硕士生导师，主要研究方向为认知无线电、人工智能、物联网等" ]
  [ "姚育东（1960− ），男，博士，史蒂文斯理工学院电子与计算机工程系终身教授。2011年当选国际电气电子工程师协会会士（IEEE Fellow），2015年当选美国国家发明家科学院院士（Fellow of National Academy of Inventors），2017年当选加拿大工程院院士（Fellow of Canadian Academy of Engineering）。主要研究方向为通信信息技术、人工智能、健康物联网" ]
  [ "杨喜（1978− ），男，博士，吉首大学信息科学与工程学院教授、硕士生导师，主要研究方向为认知无线电、统计信号处理、智能信息与信号处理" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61861019);华侨大学中央高校基本科研业务费专项;The Fundamental Research Funds for the Central Universities(ZQN-708)
- DOI：10.11959/j.issn.1000-0801.2021114
  中图分类号： TN929
- 网络出版日期：2021-05，
  
  纸质出版日期：2021-05-20
- 稿件说明：
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孙姝君, 彭盛亮, 姚育东, 等. 基于深度学习的调制识别综述[J]. 电信科学, 2021,37(5):82-90.

Shujun SUN, Shengliang PENG, Yudong YAO, et al. A survey of deep learning based modulation recognition[J]. Telecommunications science, 2021, 37(5): 82-90.
孙姝君, 彭盛亮, 姚育东, 等. 基于深度学习的调制识别综述[J]. 电信科学, 2021,37(5):82-90. DOI： 10.11959/j.issn.1000-0801.2021114.

Shujun SUN, Shengliang PENG, Yudong YAO, et al. A survey of deep learning based modulation recognition[J]. Telecommunications science, 2021, 37(5): 82-90. DOI： 10.11959/j.issn.1000-0801.2021114.

摘要

调制识别是通信系统的基础任务之一，在认知无线电、智能通信、无线电监管、电子对抗等领域均有着广泛的应用。近年来，基于深度学习的调制识别技术以其在特征提取和识别性能方面的优势，日益成为研究的焦点。系统地梳理了基于深度学习的调制识别技术，首先介绍了相关基础，随后详细阐述了其系统架构、数据预处理方式、深度神经网络结构、常用数据集以及评价指标，最后分析展望了该技术未来的发展方向。

Abstract

Modulation recognition is one of the fundamental tasks for communications systems

which can be widely applied in various fields

such as cognitive radio

intelligent communications

radio surveillance

electronic warfare

etc.In recent years

deep learning (DL) based modulation recognition has attracted great attention due to its superiority in feature extraction and recognition performance.The techniques of DL based modulation recognition were systematically summarized.Firstly

some knowledge relevant to DL based modulation recognition was introduced.Then

the system architecture

data pre-processing methods

deep neural network structures

prevalent datasets and performance metrics of DL based modulation recognition were illustrated.Finally

the future directions of DL based modulation recognition were also discussed.

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references

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