基于同时加权OMP算法的XL-MIMO混合场信道估计  

XL-MIMO hybrid-field channel estimation based on simultaneous weighted OMP algorithm

作　　者：黄欢 张钧鑫 HUANG Huan;ZHANG Junxin(College of Information Science and Technology,Tibet University,Lhasa 850011,Xizang,China)

机构地区：[1]西藏大学信息科学技术学院,西藏拉萨850011

出　　处：《山东大学学报(工学版)》2025年第1期157-164,共8页Journal of Shandong University(Engineering Science)

基　　金：西藏大学研究生高水平人才培养计划资助项目(2021-GSP-S122)。

摘　　要：针对在超大规模多输入多输出(extreme large-scale multiple-input multiple-output,XL-MIMO)系统中如何高效估计混合场信道状态信息(channel state information,CSI)和信道稀疏度不易获取的问题,提出一种联合混合场信道估计方案和同时加权正交匹配追踪(simultaneous weighted orthogonal matching pursuit,SWOMP)算法,在未知混合场信道稀疏度的情况下能够有效估计混合场信道状态信息。在算法设计过程中,采用伍德伯里变换替换SWOMP算法中的矩阵求逆,没有降低算法计算复杂度,因此提出基于理查森迭代方法变换的低复杂度SWOMP算法。将低复杂度SWOMP算法与现有算法进行比较,仿真结果表明,该方案和算法的设计具有更高的估计精度。In response to the challenge of efficiently estimating the channel state information(CSI)and channel sparsity in extreme large-scale multiple-input multiple-output(XL-MIMO)systems,a joint hybrid-field channel estimation scheme and a simultaneous weighted orthogonal matching pursuit(SWOMP)algorithm were proposed.This approach effectively estimated the hybrid-field channel state information even when the sparsity of the hybrid-field channel was unknown.In the process of algorithm design,the Woodbury transformation was employed to replace the matrix inversion in the SWOMP algorithm,thereby maintaining algorithmic computational complexity.Consequently,a low-complexity SWOMP algorithm based on Richardson iteration transformation was proposed.Comparative simulations with existing algorithms demonstrated that the proposed scheme and algorithm design achieved higher estimation accuracy.

关 键 词：XL-MIMO 信道估计 混合场 伍德伯里变换 理查森迭代 

分 类 号：TN929.5[电子电信—通信与信息系统]