Deep learning based physical layer wireless communication techniques:opportunities and challenges
The development of the fifth-generation wireless communications (5G) system is promoted by the high requirements of the high reliability and super-high network capacity.However,existing communication techniques are hard to achieve the high requirements due to the more and more complexity design in 5...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019043/ |
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| _version_ | 1841539438095630336 |
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| author | Guan GUI Yu WANG Hao HUANG |
| author_facet | Guan GUI Yu WANG Hao HUANG |
| author_sort | Guan GUI |
| collection | DOAJ |
| description | The development of the fifth-generation wireless communications (5G) system is promoted by the high requirements of the high reliability and super-high network capacity.However,existing communication techniques are hard to achieve the high requirements due to the more and more complexity design in 5G system.Currently,deep learning is considered one of effective tools to handle the physical layer wireless communications.Several potential applications based on deep learning were reviewed,and their effectiveness were confirmed.Finally,several potential techniques in deep learning based physical layer wireless communications were pointed out. |
| format | Article |
| id | doaj-art-876b1ab83864489583810db934199c6a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-876b1ab83864489583810db934199c6a2025-01-14T07:16:16ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-02-0140192359724880Deep learning based physical layer wireless communication techniques:opportunities and challengesGuan GUIYu WANGHao HUANGThe development of the fifth-generation wireless communications (5G) system is promoted by the high requirements of the high reliability and super-high network capacity.However,existing communication techniques are hard to achieve the high requirements due to the more and more complexity design in 5G system.Currently,deep learning is considered one of effective tools to handle the physical layer wireless communications.Several potential applications based on deep learning were reviewed,and their effectiveness were confirmed.Finally,several potential techniques in deep learning based physical layer wireless communications were pointed out.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019043/physical layer wireless communicationdeep learningdeep neural networkmodulation model recognitionbeamforming |
| spellingShingle | Guan GUI Yu WANG Hao HUANG Deep learning based physical layer wireless communication techniques:opportunities and challenges Tongxin xuebao physical layer wireless communication deep learning deep neural network modulation model recognition beamforming |
| title | Deep learning based physical layer wireless communication techniques:opportunities and challenges |
| title_full | Deep learning based physical layer wireless communication techniques:opportunities and challenges |
| title_fullStr | Deep learning based physical layer wireless communication techniques:opportunities and challenges |
| title_full_unstemmed | Deep learning based physical layer wireless communication techniques:opportunities and challenges |
| title_short | Deep learning based physical layer wireless communication techniques:opportunities and challenges |
| title_sort | deep learning based physical layer wireless communication techniques opportunities and challenges |
| topic | physical layer wireless communication deep learning deep neural network modulation model recognition beamforming |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019043/ |
| work_keys_str_mv | AT guangui deeplearningbasedphysicallayerwirelesscommunicationtechniquesopportunitiesandchallenges AT yuwang deeplearningbasedphysicallayerwirelesscommunicationtechniquesopportunitiesandchallenges AT haohuang deeplearningbasedphysicallayerwirelesscommunicationtechniquesopportunitiesandchallenges |