超大规模MIMO系统的离网格混合场信道估计算法

杨黎明; 谭旭; 肖清华

doi:10.11959/j.issn.1000-0801.DXKX260019

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超大规模MIMO系统的离网格混合场信道估计算法

研究与开发 | 更新时间：2026-06-17

- 超大规模MIMO系统的离网格混合场信道估计算法
- Off-grid hybrid-field channel estimation algorithm for extremely large-scale MIMO systems
- 电信科学 2026年42卷第5期页码：15-29
- 作者机构：
  
  重庆邮电大学通信与信息工程学院，重庆 400065
- 作者简介：
  
  杨黎明（1976- ），女，重庆邮电大学通信与信息工程学院高级工程师，主要研究方向为移动通信协议栈软件设计及测试、移动通信空口安全。
  谭旭（2000- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为移动通信物理层协议与信道估计。
  肖清华（2001- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为移动通信物理层协议与信道估计。
- 基金信息：
  
  重庆市自然科学基金创新发展联合基金（中国星网）(CSTB2023NSCQ-LZX0114)
- DOI：10.11959/j.issn.1000-0801.DXKX260019
  中图分类号： TN929.5
- 收稿：2026-01-08，
  
  修回：2026-02-01，
  
  录用：2026-02-10，
  
  纸质出版：2026-05-20
- 稿件说明：
移动端阅览
杨黎明,谭旭,肖清华.超大规模MIMO系统的离网格混合场信道估计算法[J].电信科学,2026,42(05):15-29.

Yang Liming,Tan Xu,Xiao Qinghua.Off-grid hybrid-field channel estimation algorithm for extremely large-scale MIMO systems[J].Telecommunications Science,2026,42(05):15-29.
杨黎明,谭旭,肖清华.超大规模MIMO系统的离网格混合场信道估计算法[J].电信科学,2026,42(05):15-29. DOI： 10.11959/j.issn.1000-0801.DXKX260019.

Yang Liming,Tan Xu,Xiao Qinghua.Off-grid hybrid-field channel estimation algorithm for extremely large-scale MIMO systems[J].Telecommunications Science,2026,42(05):15-29. DOI： 10.11959/j.issn.1000-0801.DXKX260019.

摘要

在超大规模多输入多输出（extremely large-scale multiple-input multiple-output，XL-MIMO）系统中，实现对混合场信道状态信息（channel state information，CSI）的高精度估计仍是未来6G网络高速传输的核心挑战之一。针对传统混合场信道估计算法固定网格划分导致的精度限制问题，提出了一种两阶段离网格混合场信道估计算法。该算法在第一阶段对远场角域和近场极域进行联合稀疏表示，通过遍历远近场路径比例参数并分配路径配额，利用稀疏梯度追踪在联合字典上交替搜索远近场原子，结合逐行最小均方（least mean square

LMS）算法的增量残差更新路径支撑，获得混合场信道的粗估计。在初始支撑的基础上，算法在第二阶段利用数值梯度与线搜索相结合的牛顿迭代，对路径角度和距离等参数进行精细估计，从而重构完整混合场信道。仿真结果表明，在不同信噪比、用户天线数量等场景下，所提算法的归一化均方误差（normalized mean squared error，NMSE）始终优于传统混合场信道估计算法，相比现有的离网格随机梯度追踪（stochastic gradient pursuit，SGP）算法其性能提升1.5~3 dB。

Abstract

In extremely large-scale multiple-input multiple-output (XL-MIMO) systems

accurate acquisition of hybrid-field channel state information (CSI) remains one of the key challenges for high-rate transmission in future 6G networks. To overcome the accuracy limitation caused by fixed grid partitioning in conventional hybrid-field channel estimation schemes

a two-stage off-grid hybrid-field channel estimation algorithm was developed. In the first stage

the far-field angular domain and near-field polar domain were jointly sparsely represented. By traversing the ratio between far-field and near-field paths and allocating the path quota

sparse gradient pursuit was applied to a joint dictionary to alternately search for far-field and near-field atoms

while an incremental residual update based on a row-wise least meansquare (LMS) algorithm was employed to obtain a coarse estimate of the hybrid-field channel. On the basis of the initial support

a Newton iteration combining numerical gradients and line search were used to refine continuous parameters such as path angles and distances by the second stage

thereby reconstructing the complete hybrid-field channel. Simulation results demonstrate that

for different signal-to-noise ratios and numbers of user antennas

the normalized mean square error (NMSE) of the proposed scheme is consistently lower than that of conventional hybrid-field channel estimation algorithms

and it achieves a performance gain of 1.5~3 dB compared with an existing off-grid stochastic gradient pursuit (SGP) algorithm.

关键词

Keywords

references

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