人工智能辅助的信道估计最新研究进展

doi:10.11959/j.issn.1000-0801.2020288

电信科学 ›› 2020, Vol. 36 ›› Issue (10): 46-55.doi: 10.11959/j.issn.1000-0801.2020288

人工智能辅助的信道估计最新研究进展

李坤,张静,李潇,金石

东南大学移动通信国家重点实验室，江苏南京 210096

修回日期:2020-10-10 出版日期:2020-10-20 发布日期:2020-11-07
作者简介:李坤（1995- ），男，东南大学移动通信国家重点实验室硕士生，主要研究方向为无线通信物理层技术与深度学习结合|张静（1993- ），女，东南大学移动通信国家重点实验室博士生，主要研究方向为 5G移动通信物理层关键技术、机器学习等|李潇（1982- ），女，东南大学移动通信国家重点实验室副教授、硕士生导师，主要研究方向为移动通信理论与关键技术、人工智能在无线通信中的应用等|金石（1974- ），男，东南大学移动通信国家重点实验室教授、博士生导师，主要研究方向为移动通信理论与关键技术、物联网理论与关键技术以及人工智能在无线通信中的应用等
基金资助:
国家重点研发计划项目(2018YFA0701602);国家自然科学基金杰出青年科学基金资助项目(61625106);深圳基础研究基金资助项目(JCYJ20170412104656685)

An overview of artificial intelligence assisted channel estimation

Kun LI,Jing ZHANG,Xiao LI,Shi JIN

National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China

Revised:2020-10-10 Online:2020-10-20 Published:2020-11-07
Supported by:
The National Key Research and Development Program(2018YFA0701602);The National Science Foundation for Distinguished Young Scholars of China(61625106);The Shenzhen Basic Research Foundation(JCYJ20170412104656685)

摘要/Abstract

摘要：

作为第六代移动通信发展的主流方向，智能通信正在蓬勃发展中，且初步展示了其与传统通信方法相比的优势。人工智能辅助的信道估计作为智能通信的重要组成，在已有的研究成果中展示了其相比传统信道估计算法的优越性，尤其是基于压缩感知技术、超分辨技术、残差学习等开展的信道估计研究均获得了丰硕的成果。针对人工智能辅助的信道估计技术，结合近来学术界最新研究成果，分别从基于深度卷积神经网络、基于深度循环神经网络、基于超分辨技术、基于压缩感知技术 4 个维度展示了人工智能辅助的信道估计的全貌。最后，对比总结了4类信道估计方法优劣及其未来研究方向，展望了信道估计与深度学习结合的广阔前景。

关键词: 人工智能, 信道估计, 深度学习, 压缩感知, 超分辨

Abstract:

As the mainstream of the sixth generation mobile communication development,intelligent communication assisted by artificial intelligence technology is vigorously developing,and has initially demonstrated its advantages over traditional communication methods.As an important component of intelligent communication,artificial intelligence assisted channel estimation shows its superiority over traditional channel estimation algorithms in the existing research results,especially those researches based on compressive sensing technology,super resolution technology,residual learning,etc.Aiming at the channel estimation technology assisted by artificial intelligence,combined with the latest research results in the academic field,the whole picture of the channel estimation technology assisted by artificial intelligence from the four dimensions of deep convolution neural network,deep recurrent neural network,super-resolution technology and compression sensing technology were showed.Finally,the advantages and disadvantages of four kinds of channel estimation methods and their future research directions,and the broad prospect of the combination of channel estimation and deep learning were looked forward.

Key words: artificial intelligence, channel estimation, deep learning, compressive sensing, super resolution

中图分类号:

TN929

李坤,张静,李潇,金石. 人工智能辅助的信道估计最新研究进展[J]. 电信科学, 2020, 36(10): 46-55.

Kun LI,Jing ZHANG,Xiao LI,Shi JIN. An overview of artificial intelligence assisted channel estimation[J]. Telecommunications Science, 2020, 36(10): 46-55.

图/表 3

参考文献 26

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深度学习方法	典型应用场景	特点分析
深度CNN	毫米波大规模MIMO系统	多子网联合、稳健性强、计算复杂度高
深度RNN	时变信道及高速移动场景	聚焦CSI时间相关性、LSTM网络
SR技术	OFDM通信系统	依赖图像去噪算法、稳健性较差
CS技术	毫米波大规模MIMO系统	深层展开理念、复杂的去噪器设计

人工智能辅助的信道估计最新研究进展

An overview of artificial intelligence assisted channel estimation

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