通信学报 ›› 2019, Vol. 40 ›› Issue (2): 19-23.doi: 10.11959/j.issn.1000-436x.2019043

• 专题:5G与AI • 上一篇    下一篇

基于深度学习的物理层无线通信技术:机遇与挑战

桂冠,王禹,黄浩   

  1. 南京邮电大学通信与信息工程学院,江苏 南京 210003
  • 修回日期:2019-02-14 出版日期:2019-02-01 发布日期:2019-03-04
  • 作者简介:桂冠(1982- ),男,安徽枞阳人,博士,南京邮电大学教授,主要研究方向为基于深度学习的物理层无线通信技术。|王禹(1996- ),男,江苏东台人,南京邮电大学博士生,主要研究方向为基于深度学习的物理层无线通信技术。|黄浩(1995- ),男,江苏海安人,南京邮电大学博士生,主要研究方向为基于深度学习的物理层无线通信技术。

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

Guan GUI,Yu WANG,Hao HUANG   

  1. College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Revised:2019-02-14 Online:2019-02-01 Published:2019-03-04

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。

关键词: 物理层无线通信, 深度学习, 深度神经网络, 调制模式识别, 波束成形

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.

Key words: physical layer wireless communication, deep learning, deep neural network, modulation model recognition, beamforming

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