Deep learning based physical layer wireless communication techniques:opportunities and challenges

Guan GUI; Yu WANG; Hao HUANG

doi:10.11959/j.issn.1000-436x.2019043

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Deep learning based physical layer wireless communication techniques:opportunities and challenges

Topics:5G and AI | 更新时间：2024-06-05

- Deep learning based physical layer wireless communication techniques:opportunities and challenges
- Journal on Communications Vol. 40, Issue 2, Pages: 19-23(2019)
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2019043
  CLC： TN929.5
- Online First：2019-02，
  
  Published：25 February 2019
- 稿件说明：
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Guan GUI, Yu WANG, Hao HUANG. Deep learning based physical layer wireless communication techniques:opportunities and challenges[J]. Journal on Communications, 2019, 40(2): 19-23.
DOI：

Guan GUI, Yu WANG, Hao HUANG. Deep learning based physical layer wireless communication techniques:opportunities and challenges[J]. Journal on Communications, 2019, 40(2): 19-23. DOI： 10.11959/j.issn.1000-436x.2019043.

摘要

对无线通信系统的高可靠性与超高容量需求促进了第五代移动通信（5G）的发展，然而，随着通信系统的日益复杂，现有的物理层无线通信技术难以满足这些高的性能需求。目前，深度学习被认为是处理物理层通信的有效工具之一，基于此，主要探讨了深度学习在物理层无线通信中的潜在应用，并且证明了其卓越性能。最后，提出几个可能发展的基于深度学习的物理层无线通信技术。

Abstract

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.

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Keywords

references

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