基于深度学习的信源信道联合编码方法综述

穆天杰; 陈晓辉; 汪逸云; 马陆鹏; 刘东; 周晶; 张文逸

doi:10.11959/j.issn.1000-0801.2020290

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基于深度学习的信源信道联合编码方法综述

专题：智能通信技术 | 更新时间：2024-06-05

- 基于深度学习的信源信道联合编码方法综述
- A survey on deep learning based joint source-channel coding
- 电信科学 2020年36卷第10期页码：56-66
- 作者机构：
- 作者简介：
  
  [ "穆天杰（1998- ），男，中国科学技术大学硕士生，主要研究方向为新型无线收发信机设计" ]
  [ "陈晓辉（1976- ），男，中国科学技术大学副教授，主要研究方向为无线空口协议、多媒体通信等" ]
  [ "汪逸云（1997- ），女，中国科学技术大学硕士生，主要研究方向为压缩感知、信道估计等" ]
  [ "马陆鹏（1994- ），男，中国科学技术大学硕士生，主要研究方向为新型信源编码方法" ]
  [ "刘东（1983- ），男，中国科学技术大学副教授，主要研究方向为图像、视频处理等" ]
  [ "周晶（1983- ），男，博士，中国科学技术大学在读博士后，主要研究方向为信息论、无线通信等" ]
  [ "张文逸（1979- ），男，中国科学技术大学教授，主要研究方向为信息论、无线通信等" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Program of China(2018YFA0701603)
- DOI：10.11959/j.issn.1000-0801.2020290
  中图分类号： TN929.5
- 网络出版日期：2020-10，
  
  纸质出版日期：2020-10-20
- 稿件说明：
移动端阅览
穆天杰, 陈晓辉, 汪逸云, 等. 基于深度学习的信源信道联合编码方法综述[J]. 电信科学, 2020,36(10):56-66.

Tianjie MU, Xiaohui CHEN, Yiyun WANG, et al. A survey on deep learning based joint source-channel coding[J]. Telecommunications science, 2020, 36(10): 56-66.
穆天杰, 陈晓辉, 汪逸云, 等. 基于深度学习的信源信道联合编码方法综述[J]. 电信科学, 2020,36(10):56-66. DOI： 10.11959/j.issn.1000-0801.2020290.

Tianjie MU, Xiaohui CHEN, Yiyun WANG, et al. A survey on deep learning based joint source-channel coding[J]. Telecommunications science, 2020, 36(10): 56-66. DOI： 10.11959/j.issn.1000-0801.2020290.

摘要

信息论的经典结果表明，信源信道分离编码是渐进最优的。但现代通信系统对时延、带宽等愈发敏感，分离设计对解码具有无限计算能力这一假设难以成立。带宽有限时，相对于信源信道联合编码，分离编码已被证明是次优的。传统的联合信源信道编码需要复杂的编码方案，相较之下，数据驱动的深度学习技术则带来了新的设计思路。适时地对相关研究成果进行总结，有助于进一步明确深度学习方法解决信源信道联合编码问题的方式，为研究新的研究方向提供依据。首先介绍了基于深度学习的信源压缩方案和端对端收发信机模型，随后分析不同信源类型下的两种联合编码设计思路，最后探讨了基于深度学习的信源信道联合编码的潜在问题和未来的工作方向。

Abstract

Classical information theory shows that separate source-channel coding is asymptotically optimal over a point-to-point channel.As modern communication systems are becoming more sensitive to delays and bandwidth

it becomes difficult to adopt the assumption that such separate designs have unlimited computing power for encoding and decoding.Compared to joint source-channel coding

separate coding has proven to be sub-optimal when the bandwidth is limited.However

conventional joint source-channel coding schemes require complicated design.In contrast

data-driven deep learning brings new designing ideas into the paradigm.A summary of relevant research results was provided

which will help to clarify the way in which deep learning methods solve the joint source-channel coding problem and to provide an overviewof new research directions.Source compression schemes and end-to-end communication system models were firstly introduced

both based on deep learning

then two kinds of joint coding designs under different types of source

and potential problems of joint source-channel coding based on deep learning and possible future research directions were introduced.

关键词

Keywords

references

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