基于深度学习的大规模MIMO信道状态信息反馈

陈慕涵; 郭佳佳; 李潇; 金石

doi:10.11959/j.issn.2096-3750.2020.00157

您当前的位置：

首页 >

文章列表页 >

基于深度学习的大规模MIMO信道状态信息反馈

专题：物联网与6G | 更新时间：2024-06-05

- 基于深度学习的大规模MIMO信道状态信息反馈
- An overview of the CSI feedback based on deep learning for massive MIMO systems
- 物联网学报 2020年4卷第1期页码：33-44
- 作者机构：
- 作者简介：
  
  [ "陈慕涵（1997- ），女，江苏南通人，东南大学移动通信国家重点实验室硕士生，主要研究方向为基于深度学习的信道状态信息反馈、机器学习等" ]
  [ "郭佳佳（1993- ），男，江苏泰兴人，东南大学移动通信国家重点实验室博士生，主要研究方向为基于深度学习的信道状态信息反馈等" ]
  [ "李潇（1982- ），女，安徽蚌埠人，博士，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、智能通信以及智能反射表面在无线通信中的应用等" ]
  [ "金石（1974- ），男，安徽黄山人，博士，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" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61941104)
- DOI：10.11959/j.issn.2096-3750.2020.00157
  中图分类号： TN929.5
- 纸质出版日期：2020-03-30，
  
  网络出版日期：2020-03，
- 稿件说明：
移动端阅览
陈慕涵, 郭佳佳, 李潇, 等. 基于深度学习的大规模MIMO信道状态信息反馈[J]. 物联网学报, 2020,4(1):33-44.

MUHAN CHEN, JIAJIA GUO, XIAO LI, et al. An overview of the CSI feedback based on deep learning for massive MIMO systems. [J]. Chinese journal on internet of things, 2020, 4(1): 33-44.
陈慕涵, 郭佳佳, 李潇, 等. 基于深度学习的大规模MIMO信道状态信息反馈[J]. 物联网学报, 2020,4(1):33-44. DOI： 10.11959/j.issn.2096-3750.2020.00157.

MUHAN CHEN, JIAJIA GUO, XIAO LI, et al. An overview of the CSI feedback based on deep learning for massive MIMO systems. [J]. Chinese journal on internet of things, 2020, 4(1): 33-44. DOI： 10.11959/j.issn.2096-3750.2020.00157.

摘要

大规模多输入多输出（MIMO

multiple-input multiple output）技术被认为是下一代移动通信的核心技术之一，其系统增益建立在基站能够精确获知信道状态信息（CSI

channel state information）的基础上。由于天线数量显著增长，传统基于码本或矢量量化的反馈方案面临较大的技术挑战，而深度学习（DL

deep learning）为解决大规模MIMO系统的CSI反馈问题提供了新思路。围绕大规模MIMO系统CSI反馈关键技术展开调研，首先阐述了CSI反馈的研究背景和意义，接着构建大规模MIMO系统模型并分析CSI的稀疏特性，然后详细介绍和比较了国内外将DL技术引入CSI反馈机制中的方案，最后对基于DL的CSI反馈的未来发展趋势做了进一步展望。

Abstract

The massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems

the base station should accurately acquire the channel state information (CSI).Due to the significant increase in the number of antennas

the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently

deep learning (DL) has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly

the background and significance of the CSI feedback were expounded.Then

a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally

a further prospect on the development trend of the CSI feedback based on DL was made.

关键词

大规模MIMO深度学习CSI反馈

Keywords

massive MIMOdeep learningCSI feedback

references

WONG V S, SCHOBER R, NG D W K ,et al. Key technologies for 5G wireless systems[M]. Cambridge: Cambridge University PressPress, 2017.

YANG P, XIAO Y, XIAO M ,et al. 6G wireless communications:vision and potential techniques[J]. IEEE Network, 2019,33(4): 70-75.

LOVE D J, HEATH R W, LAU V K N ,et al. An overview of limited feedback in wireless communication systems[J]. IEEE Journal on Selected Areas in Communications, 2008,26(8): 1341-1365.

ZHOU Y, HERDIN M, SAYEED A M ,et al. Experimental study of MIMO channel statistics and capacity via the virtual channel representation[D]. Madison:University of Wisconsin-Madison, 2007.

KYRITSI P, COX D C, VALENZUELA R A ,et al. Correlation analysis based on MIMO channel measurements in an indoor environment[J]. IEEE Journal on Selected Areas in Communications, 2003,21(5): 713-720.

KUO P H, KUNG H T, TING P A . Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays[C]// 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012: 492-497.

RAO X B, LAU V K . Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems[J]. IEEE Transactions on Signal Processing, 2014,62(12): 3261-3271.

SHEA T O, HOYDIS J . An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017,3(4): 563-575.

QIN Z J, YE H, LI G Y ,et al. Deep learning in physical layer communications[J]. IEEE Wireless Communications, 2019,26(2): 93-99.

张静, 金石, 温朝凯 ,等. 基于人工智能的无线传输技术最新研究进展[J]. 电信科学, 2018,34(8): 46-55.

ZHANG J, JIN S, WEN C K ,et al. An overview of wireless transmission technology utilizing artificial intelligence[J]. Telecommunications Science, 2018,34(8): 46-55.

WANG T Q, WEN C K, WANG H Q ,et al. Deep learning for wireless physical layer:opportunities and challenges[J]. China Communications, 2017,14(11): 92-111.

WEN C K, JIN S, WONG K K ,et al. Channel estimation for massive MIMO using Gaussian-mixture Bayesian learning[J]. IEEE Transactions on Wireless Communications, 2015,14(3): 1356-1368.

DAUBECHIES I, DEFRISE M, MOL C D . An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 2003,57(11): 1413-1457.

DONOHO D L, MALEKI A, MONTANARI A . Message passing algorithms for compressed sensing[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009,106(45): 18914-18919.

LI C, YIN W, ZHANG Y ,et al. TVAL3:TV minimization by augmented Lagrangian and alternating direction algorithms[E/OL]. 20132013.

METZLER C A, MALEKI A, BARANIUK R G . From denoising to compressed sensing[J]. IEEE Transactions on Information Theory, 2016,62(9): 5117-5144.

LOHIT S, KULKARNI K, KERVICHE R ,et al. Convolutional neural networks for non-iterative reconstruction of compressively sensed images[J]. IEEE Transactions on Computational Imaging, 2018,4(3): 326-340.

MOUSAVI A, DASARATHY G, BARANIUK R G . DeepCodec:adaptive sensing and recovery via deep convolutional neural networks[J]. arXiv:1707.03386, 2017

WEN C K, SHIH W T, JIN S . Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018,7(5): 748-751.

VINCENT P, LAROCHELLE H, BENGIO Y ,et al. Extracting and composing robust features with denoising autoencoders[C]// Proceedings of the 25th International Conference on Machine Learning (ICML). ACM, 2008: 1096-1103.

LIU L F, OESTGES C, POUTANEN J ,et al. The COST 2100 MIMO channel model[J]. IEEE Wireless Communications, 2012,19(6): 92-99.

XU K, REN F B . CSVideoNet:a real-time end-to-end learning framework for high-frame-rate video compressive sensing[C]// 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018: 1680-1688.

WANG T Q, WEN C K, JIN S ,et al. Deep learning-based CSI feedback approach for time-varying massive MIMO channels[J]. IEEE Wireless Communications Letters, 2019,8(2): 416-419.

LU C, XU W, SHEN H ,et al. MIMO channel information feedback using deep recurrent network[J]. IEEE Communications Letters, 2019,23(1): 188-191.

LIU Z Y, ZHANG L, DING Z . Exploiting bi-directional channel reciprocity in deep learning for low rate massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2019,8(3): 889-892.

WU P X, LIU Z C, CHENG J L . Compressed CSI feedback with learned measurement matrix for mmWave massive MIMO[J]. arXiv:1903.02127, 2019

LI X Y, WU H M . Spatio-temporal representation with deep neural recurrent network in MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2020

CAI Q Y, DONG C, NIU K . Attention model for massive MIMO CSI compression feedback and recovery[C]// 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2019.

GUO J J, WANG J H, WEN C K ,et al. Compression and acceleration of neural networks for communications[J]. arXiv:1907.13269, 2019

浏览量

2135

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

一种基于深度学习的物联网信道状态信息获取算法

基于AE和Transformer的运动想象脑电信号分类研究

一种基于深度可分离卷积和注意力机制的入侵检测方法

基于多智能体深度强化学习的多域协同抗干扰方法研究

物联网环境中基于深度学习的差分隐私预算优化方法