基于压缩感知的大规模MIMO信道估计与反馈

毛攀; 黄小光; 汪伟; 宋建

doi:10.11959/j.issn.1000-0801.2018288

您当前的位置：

首页 >

文章列表页 >

基于压缩感知的大规模MIMO信道估计与反馈

研究与开发 | 更新时间：2024-06-05

- 基于压缩感知的大规模MIMO信道估计与反馈
- Massive MIMO channel estimation and feedback based on compressed sensing
- 电信科学 2018年34卷第12期页码：46-52
- 作者机构：
- 作者简介：
  
  [ "毛攀（1991-），男，华信咨询设计研究院有限公司助理工程师，主要研究方向为无线网规划和设计。" ]
  [ "黄小光（1983-），男，华信咨询设计研究院有限公司高级工程师，主要研究方向为无线网络规划、优化和设计。" ]
  [ "汪伟（1980-），男，华信咨询设计研究院有限公司高级工程师，主要研究方向为无线网规划、优化和设计。" ]
  [ "宋建（1984-），男，华信咨询设计研究院有限公司工程师，主要研究方向为无线网规划和设计。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018288
  中图分类号： TN929.5
- 网络出版日期：2018-12，
  
  纸质出版日期：2018-12-20
- 稿件说明：
移动端阅览
毛攀, 黄小光, 汪伟, 等. 基于压缩感知的大规模MIMO信道估计与反馈[J]. 电信科学, 2018,34(12):46-52.

Pan MAO, Xiaoguang HUANG, Wei WANG, et al. Massive MIMO channel estimation and feedback based on compressed sensing[J]. Telecommunications science, 2018, 34(12): 46-52.
毛攀, 黄小光, 汪伟, 等. 基于压缩感知的大规模MIMO信道估计与反馈[J]. 电信科学, 2018,34(12):46-52. DOI： 10.11959/j.issn.1000-0801.2018288.

Pan MAO, Xiaoguang HUANG, Wei WANG, et al. Massive MIMO channel estimation and feedback based on compressed sensing[J]. Telecommunications science, 2018, 34(12): 46-52. DOI： 10.11959/j.issn.1000-0801.2018288.

摘要

针对大规模 MIMO系统信道估计精度低及反馈方案较为复杂的问题，在差分信道估计及反馈方案上提出了一种基于系数相关性的压缩采样匹配追踪（BCC-CoSAMP）算法。该算法将CoSAMP算法中衡量两个向量之间关系的内积替换为基于相关系数的向量关系判定，从而较快地选出与原始信号相关性强的原子，达到提高信道估计精度的目的。仿真结果表明，与CoSAMP算法相比，所提出的BCC-CoSAMP算法在低信噪比情况下，信道估计精度平均有5 dB的提高，同时能平均提高系统总速率1.25 bit/(s.Hz)。

Abstract

Focused on the issue that the problem of low precision of channel estimation and complex feedback scheme in massive MIMO system

a compression sampling matching pursuit (BCC-CoSAMP) algorithm based on coefficient correlation was proposed in the differential channel estimation and feedback scheme.In this algorithm

the inner product of the relationship between two vectors in the CoSAMP algorithm was replaced by the vector relation based on the correlation coefficient

so the atoms with strong correlation of the original signal were quickly selected to improve the accuracy of channel estimation.The simulation results show that the proposed BCC-CoSAMP algorithm has an improved channel estimation accuracy compared with the CoSAMP algorithm and an average increase of the total system rate of 1.25 bit/(s.Hz) at low signal to noise ratio (SNR).

关键词

Keywords

references

LU L , LI G Y , SWINDLEHURST A L , et al . An overview of massive MIMO:benefits and challenges [J ] . IEEE Journal of Selected Topics in Signal Processing , 2014 , 8 ( 5 ): 742 - 758 .

陆莹 . 能效优化的大规模 MIMO 无线传输技术 [D ] . 南京:东南大学 , 2016 .

LU Y . Large scale MIMO wireless transmission technology with energy efficiency optimization [D ] . Nanjing:Southeast University , 2016 .

CHOI J , LOVE D J , BIDIGARE P . Downlink training techniques for FDD massive MIMO systems:open-loop and closed-loop training with memory [J ] . IEEE Journal of Selected Topics in Signal Processing , 2014 , 8 ( 5 ): 802 - 814 .

陈慧慧 . 大规模 MIMO 系统中基于信道压缩的有限反馈技术研究 [D ] . 重庆:重庆邮电大学 , 2016 .

CHEN H H . Research on limited feedback techniques based on compressed channel in massive MIMO systems [D ] . Chongqing:Chongqing University of Posts and Telecommunications , 2016 .

张梦莹 . 基于CS的大规模MIMO信道估计技术研究 [D ] . 南京:南京邮电大学 , 2017 .

ZHANG M Y . Research on channel estimation with compressive sensing for massive MIMO [D ] . Nanjing:Nanjing University of Posts and Telecommunications , 2017 .

张梦莹 , 陈璇 . 基于压缩感知的大规模 MIMO 分段信道反馈 [J ] . 计算机技术与发展 , 2017 , 27 ( 6 ): 183 - 186 .

ZHANG M Y , CHEN X . Segmental channel feedback for massive MIMO with compressive sensing [J ] . Computer Technology and Development , 2017 , 27 ( 6 ): 183 - 186 .

DAI L , GAO Z , WANG Z . Joint channel estimation and feedback with low overhead for FDD massive MIMO systems [C ] // IEEE/CIC International Conference on Communications in China,July 27-29,2016,Chengdu,China . Piscataway:IEEE Press , 2016 : 1 - 6 .

HUANG W , HUANG Y , XU W , et al . Beam-blocked channel estimation for FDD massive MIMO with compressed feedback [J ] . IEEE Access , 2017 ( 99 ):1.

SHEN W , DAI L , SHI Y , et al . Joint channel training and feedback for FDD massive MIMO systems [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 10 ): 8762 - 8767 .

ZINIEL J , SCHNITER P . Dynamic compressive sensing of time-varying signals via approximate message passing [J ] . IEEE Transactions on Signal Processing , 2012 , 61 ( 21 ): 5270 - 5284 .

ZHU X , DAI L , GUI G , et al . Structured matching pursuit for reconstruction of dynamic sparse channels [C ] // IEEE Global Communications Conference,Dec 6-10,2015,San Diego,California,USA . Piscataway:IEEE Press , 2015 : 1 - 5 .

何业慎 , 梁琨 . 一种压缩感知电力线信道估计机制 [J ] . 电信科学 , 2016 , 32 ( 11 ): 77 - 81 .

HE Y S , LIANG K . A power line channel estimation mechanism based on compressed sensing [J ] . Telecommunications Science , 2016 , 32 ( 11 ): 77 - 81 .

杨桄 . 图像压缩感知的重构算法研究 [D ] . 南京:南京理工大学 , 2017 .

YANG H . Research on image reconstruction algorithm based on compressed sensing [D ] . Nanjing:Nanjing University of Science and Technology , 2017 .

WANG A , WANG Y , JIANG L . Improved sparse channel estimation for multi-user massive MIMO systems with compressive sensing [C ] // International Conference on Wireless Communications ＆ Signal Processing,Oct 15-17,2015,Nanjing,China . Piscataway:IEEE Press , 2015 : 1 - 5 .

申敏 , 徐浩 , 何云 , 等 . 毫米波 MIMO 系统中迭代最小均方误差混合波束成形算法 [J ] . 电信科学 , 2017 , 33 ( 8 ): 77 - 84 .

SHEN M , XU H , HE Y , et al . Alternating minimum mean square error hybrid beamforming algorithm in mmWave MIMO system [J ] . Telecommunications Science , 2017 , 33 ( 8 ): 77 - 84 .

浏览量

788

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ADMM的多用户联合的RIS信道估计方案

基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法

太赫兹频段高速移动场景参考信号方案研究

基于复数生成对抗网络的5G OFDM信道估计方法

基于1D-Concatenate的信道估计DNN模型优化方法