宽带毫米波大规模MIMO系统中低复杂度波束域信道估计

邱佳锋

doi:10.11959/j.issn.1000-0801.2020183

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宽带毫米波大规模MIMO系统中低复杂度波束域信道估计

研究与开发 | 更新时间：2024-06-05

- 宽带毫米波大规模MIMO系统中低复杂度波束域信道估计
- Low-complexity beamspace channel estimation for wideband millimeter-wave massive MIMO system
- 电信科学 2020年36卷第8期页码：122-129
- 作者机构：
- 作者简介：
  
  [ "邱佳锋（1995- ），男，杭州电子科技大学通信工程学院硕士生，主要研究方向为毫米波大规模MIMO系统信道估计" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province of China(LY12F01008)
- DOI：10.11959/j.issn.1000-0801.2020183
  中图分类号： TN911.22
- 网络出版日期：2020-08，
  
  纸质出版日期：2020-08-20
- 稿件说明：
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邱佳锋. 宽带毫米波大规模MIMO系统中低复杂度波束域信道估计[J]. 电信科学, 2020,36(8):122-129.

Jiafeng QIU. Low-complexity beamspace channel estimation for wideband millimeter-wave massive MIMO system[J]. Telecommunications science, 2020, 36(8): 122-129.
邱佳锋. 宽带毫米波大规模MIMO系统中低复杂度波束域信道估计[J]. 电信科学, 2020,36(8):122-129. DOI： 10.11959/j.issn.1000-0801.2020183.

Jiafeng QIU. Low-complexity beamspace channel estimation for wideband millimeter-wave massive MIMO system[J]. Telecommunications science, 2020, 36(8): 122-129. DOI： 10.11959/j.issn.1000-0801.2020183.

摘要

针对宽带毫米波大规模MIMO系统信道估计精度低及实现复杂度较高的问题，在传统支撑检测方案的基础上提出一种基于Gauss-Seidel方法的串行支撑检测（GS-SSD）方案。该方案不使用共同支撑假设，参考串行干扰删除，将整体信道估计问题分解为一系列子问题，每个子问题仅考虑一个信道成分。同时，利用Gauss-Seidel方法近似高复杂度的矩阵求逆。仿真结果表明，相比于基于串行支撑检测（SSD）的方案， GS-SSD方案在将求逆复乘数降低一个数量级的同时可以取得接近SSD方案的信道估计性能。

Abstract

Focused on the issue in which the problem of low precision of channel estimation and high implementation complexity in wideband millimeter-wave(mmWave) MIMO with lens antenna array system

a successive support detection(GS-SSD) algorithm based on Gauss-Seidel method was proposed in the traditional support detection scheme.In this algorithm

the assumption of common support was not used

and inspired by the successive interference cancellation.This algorithm decomposed the total channel estimation problem into several channel component.In the meanwhile

the Gauss-Seidel method was used to approximate the matrix inversion with high complexity.Simulations were provided to show that the proposed GS-SSD algorithm could significantly reduce the complex multiplication and achieved high performance compared with successive support detection algorithm.

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references

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