Off-grid hybrid-field channel estimation algorithm for extremely large-scale MIMO systems

Yang Liming; Tan Xu; Xiao Qinghua

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

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Off-grid hybrid-field channel estimation algorithm for extremely large-scale MIMO systems

Research and Development | 更新时间：2026-06-17

- Off-grid hybrid-field channel estimation algorithm for extremely large-scale MIMO systems
- Telecommunications Science Vol. 42, Issue 5, Pages: 15-29(2026)
- 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：
- 基金信息：
  
  Chongqing Natural Science Foundation Innovation and Development Joint Fund (SatNet)(CSTB2023NSCQ-LZX0114)
- DOI：10.11959/j.issn.1000-0801.DXKX260019
  CLC： TN929.5
- Received：08 January 2026，
  
  Revised：2026-02-01，
  
  Accepted：10 February 2026，
  
  Published：20 May 2026
- 稿件说明：
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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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