A survey on deep learning based joint source-channel coding
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 co...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2020-10-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020290/ |
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| _version_ | 1841528970920591360 |
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| author | Tianjie MU Xiaohui CHEN Yiyun WANG Lupeng MA Dong LIU Jing ZHOU Wenyi ZHANG |
| author_facet | Tianjie MU Xiaohui CHEN Yiyun WANG Lupeng MA Dong LIU Jing ZHOU Wenyi ZHANG |
| author_sort | Tianjie MU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc61bdf7e1b24f4eb6c732a562aeb44e |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2020-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-dc61bdf7e1b24f4eb6c732a562aeb44e2025-01-15T03:31:53ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012020-10-0136566659812726A survey on deep learning based joint source-channel codingTianjie MUXiaohui CHENYiyun WANGLupeng MADong LIUJing ZHOUWenyi ZHANGClassical 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020290/deep learningimage/video compressionend-to-end transceiverjoint source channel coding |
| spellingShingle | Tianjie MU Xiaohui CHEN Yiyun WANG Lupeng MA Dong LIU Jing ZHOU Wenyi ZHANG A survey on deep learning based joint source-channel coding Dianxin kexue deep learning image/video compression end-to-end transceiver joint source channel coding |
| title | A survey on deep learning based joint source-channel coding |
| title_full | A survey on deep learning based joint source-channel coding |
| title_fullStr | A survey on deep learning based joint source-channel coding |
| title_full_unstemmed | A survey on deep learning based joint source-channel coding |
| title_short | A survey on deep learning based joint source-channel coding |
| title_sort | survey on deep learning based joint source channel coding |
| topic | deep learning image/video compression end-to-end transceiver joint source channel coding |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020290/ |
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