Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?

被引:68
|
作者
Wei, Xiuhong [1 ]
Dai, Linglong [1 ]
机构
[1] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Dept Elect Engn, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Channel estimation; Channel models; Transmission line matrix methods; Transforms; Antenna arrays; Sparse matrices; Estimation; Extremely large-scale massive MIMO; hybrid-field channel modeling; channel estimation;
D O I
10.1109/LCOMM.2021.3124927
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Extremely large-scale massive MIMO (XL-MIMO) is a promising technique for future 6G communications. However, existing far-field or near-field channel model mismatches the hybrid-field channel feature in the practical XL-MIMO system. Thus, existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the hybrid-field XL-MIMO channel. To solve this problem, we propose an efficient hybrid-field channel estimation scheme by accurately modeling the XL-MIMO channel. Specifically, we firstly reveal the hybrid-field channel feature of the XL-MIMO channel, where different scatters may be in far-field or near-field region. Then, we propose a hybrid-field channel model to capture this feature, which contains both the far-field and near-field path components. Finally, we propose a hybrid-field channel estimation scheme, where the far-field and near-field path components are respectively estimated. Simulation results show that the proposed scheme performs better than existing schemes.
引用
收藏
页码:177 / 181
页数:5
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