| 摘要: |
| 针对6G超大规模多输入多输出(Extremely Large-scale Multiple-Input Multiple-Output,XL-MIMO)系统信道特性变化造成现有的近场和远场信道估计方案不能准确估计XL-MIMO混合场信道的问题，同时考虑到实际系统中稀疏度难以获取，提出了一种基于分段弱正交匹配追踪的混合场信道估计(Hybrid-field Stagewise Weak Orthogonal Matching Pursuit,HF-SWOMP)算法。该算法利用XL-MIMO混合场中近场和远场区域不同的信道特性，分别对近场和远场信道分量进行估计，从而得到混合场信道。仿真结果表明，所提XL-MIMO混合场信道估计算法性能相对于仅考虑近场和远场信道估计方案分别提高了约3.5 dB和3 dB，更符合实际信道场景。 |
| 关键词: 超大规模多输入多输出 混合场 信道估计 压缩感知 自适应稀疏度 |
| DOI：10.20079/j.issn.1001-893x.230112001 |
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| 基金项目:重庆市自然科学基金面上项目(cstc2021jcyj-msxmX0454) |
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| An Adaptive Sparsity Algorithm for Hybrid-field Channel Estimation |
| WANG Huahua,GONG Zihao,JIANG Tianyu |
| (School of Communications and Information Engineering，Chongqing University of Posts and Telecommunications，Chongqing 400065,China) |
| Abstract: |
| For the problem that the existing near-field and far-field channel estimation schemes cannot accurately estimate the extremely large-scale multiple-input multiple-output(XL-MIMO) hybrid-field channel due to the change of XL-MIMO system channel characteristics,and considering that the sparsity is difficult to obtain in the actual system,a hybrid-field channel estimation algorithm based on stagewise weak orthogonal matching pursuit(HF-SWOMP) is proposed.The algorithm takes advantage of the different channel characteristics in the near-field and far-field regions of the XL-MIMO hybrid field to estimate the near-field and far-field channel components separately,thus obtaining the hybrid field channel.Simulation results show that the performance of the proposed XL-MIMO mixed-field channel estimation algorithm is improved by about 3.5 dB and 3 dB compared with the near-field and far-field channel estimation schemes respectively,which is more in line with the actual channel scenario. |
| Key words: XL-MIMO mixed fields channel estimation compressed sensing adaptive sparsity |
