Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications

Youhua FU; Dong CHEN

doi:10.11959/j.issn.1000-436x.2021197

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Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications

Papers | 更新时间：2024-06-05

- Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications
- Journal on Communications Vol. 42, Issue 10, Pages: 189-196(2021)
- 作者机构：
  
  1. 南京邮电大学电子与光学工程学院微电子学院，江苏南京 210023
  2. 射频集成与微组装技术国家地方联合工程实验室，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61771257)
- DOI：10.11959/j.issn.1000-436x.2021197
  CLC： TN928
- Online First：2021-10，
  
  Published：25 October 2021
- 稿件说明：
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Youhua FU, Dong CHEN. Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications[J]. Journal on Communications, 2021, 42(10): 189-196.
DOI：

Youhua FU, Dong CHEN. Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications[J]. Journal on Communications, 2021, 42(10): 189-196. DOI： 10.11959/j.issn.1000-436x.2021197.

摘要

为应对毫米波通信加入智能反射表面（IRS）进行辅助通信导致系统变复杂难以获得信道状态信息（CSI）这一挑战，采用了混合智能反射表面结构，即IRS由大量无源元件和有限的射频（RF）链构成，其中有限的RF链用来估计基站/终端和IRS之间的信道。基于该结构，提出一种信道估计方案。该方案根据有限的RF链，首先使用一种改进的多信号分类算法对信道的离开角和到达角同时进行估计，然后提出一种复数并联深度神经网络对路径增益进行估计。通过将所提方案和其他方法进行仿真比较，证明了所提方案的优越性。

Abstract

When adding intelligent reflecting surface (IRS) for assist communication in millimeter wave communication

the system becomes complicated and difficult to obtain channel state information (CSI).To solve these challenges

a hybrid intelligent reflecting surface structure was adopted

that is

the IRS was composed of a large number of passive elements and the limited radio frequency (RF) chains

where the limited RF chains were used to estimate the channel between the base station/terminal and the IRS.Based on the structure

a channel estimation scheme was proposed

which was based on the limited RF chains.First

an improved multiple signal classification algorithm was used to estimate the departure angle and arrival angle of the channel at the same time

and then a complex parallel deep neural network was proposed to estimate the path gain.Through simulation and comparison between the proposed scheme and other methods

the superiority of the proposed scheme is proved.

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Keywords

references

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