太赫兹超大规模MIMO混合场信道估计

doi:10.12305/j.issn.1001-506X.2025.08.32

摘要/Abstract

摘要：

超大规模多输入多输出（extremely large-scale multiple-input-multiple-output，XL-MIMO）已经成为未来6G通信的创新技术。在XL-MIMO无线系统中，由于基站天线数量的增加，信道建模需要同时考虑近场球面波和远场平面波。为了减少信道估计的导频开销，提出了一种基于凸优化方法的混合场信道估计方案，上行混合场XL-MIMO系统信道经过远场角域变换和近场极域变换后呈现稀疏性，信道估计问题为具有$ {\ell _0} $范数约束的优化问题。由于所得到的问题的非凸性，通过将$ {\ell _0} $范数放松为$ {\ell _1} $范数，将问题转化为凸问题，分别结合近端梯度（proximal gradient，PG）和交替方向乘子法（alternating direction method of multipliers，ADMM）提出两种混合场信道估计算法。太赫兹无线通信系统不同频率子载波不能近似相等，因此对不同频率的子载波使用相应频率下的字典。相比于传统的基于正交跟踪匹配的近场和远场的信道估计算法，所提的混合场算法不需要近场和远场的路径数目作为先验条件，且有更高的精度。

关键词: 混合场, 稀疏性, 信道估计, 太赫兹

Abstract:

The extremely large-scale multiple-input-multiple-output （XL-MIMO） has become an innovative technique for the future 6G communications. In XL-MIMO wireless systems, due to the increase in the number of antennas at the base station, channel modeling needs to consider both the near-field spherical waves and the far-field plane waves. In order to reduce the guide frequency overhead of channel estimation, a hybrid-field channel estimation method based on a convex optimization approach is proposed. The uplink hybrid-field XL-MIMO system channel shows sparsity after the far-field angular-domain transformations and the near-field polar-domain transformations, and the channel estimation problem is an optimization problem with $ {\ell _0} $ norm constraints. Due to the nonconvexity of the obtained problem, the problem is transformed into a convex problem by relaxing the number of $ {\ell _0} $ norms to $ {\ell _1} $ norms, and two hybrid field channel estimation algorithms are proposed by applying the proximal gradient （PG） approach and the alternating direction method of multipliers （ADMM）, respectively. The different frequency subcarriers of a wireless communication terahertz system cannot be approximated equal, so the dictionaries at the corresponding frequencies are used for the different frequency subcarriers. Compared with the traditional channel estimation algorithms based on the orthogonal tracking matching for the near and far fields, the proposed hybrid-field algorithm does not require the number of paths in the near and far field as priori condition and has higher accuracy.

Key words: hybrid field, sparsity, channel estimation, terahertz

中图分类号:

TN 929.5

高波, 傅友华, 刘陈. 太赫兹超大规模MIMO混合场信道估计[J]. 系统工程与电子技术, 2025, 47(8): 2744-2752.

Bo GAO, Youhua FU, Chen LIU. Terahertz extremely large-scale MIMO hybrid field channel estimation[J]. Systems Engineering and Electronics, 2025, 47(8): 2744-2752.

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