用于毫米波移动天线MIMO系统的基于张量的动态信道估计

中文摘要 AI 辅助

本文研究毫米波移动天线（MA）多输入多输出（MIMO）系统中的动态信道估计算法。为实现高精度信道估计，提出基于张量分解的信道估计算法。利用路径响应模型及毫米波信道固有稀疏性，将基站与移动站MA对的信道转化为稀疏路径信道叠加。把接收信号构建为四阶张量以捕捉MA MIMO信道高维结构信息。采用两种张量分解方案提取因子矩阵，分析表明模型中分解具有唯一性。基于这些因子矩阵获取传播损耗、频率偏移、到达/离开角度和时延并重建信道矩阵。推导克拉美罗界（CRB）作为性能评估标准，证明算法实现更高估计精度且接近最小界。选择归一化均方误差（NMSE）作为估计精度评估指标。仿真结果显示与基线算法相比，该算法估计误差显著降低，证实其估计优势。

英文摘要

This paper investigates the dynamic channel estimation algorithm in mmWave movable antenna (MA) multiple-input multiple-output (MIMO) systems. To achieve highly accurate channel estimation, we propose a tensor decomposition-based channel estimation algorithm. First, by leveraging the path response model and utilizing the intrinsic sparsity of mmWave channels, the channel corresponding to MA pairs at the base station and mobile station is transformed into a superposition of channels from sparse paths. Next, the received signal is constructed as a fourth-order tensor to fully capture the high-dimensional structural information of the MA MIMO channel. Then, two tensor decomposition schemes are adopted to extract the factor matrices, and our analysis reveals that the uniqueness of the decomposition can be guaranteed in our model. Subsequently, the propagation loss, frequency offset, angle of arrival/departure, and time delay are obtained based on these factor matrices and the channel matrix can be rebuilt. Additionally, Cramér-Rao bound (CRB) is also derived as a performance evaluation standard, proving that the proposed algorithm achieves a higher estimation accuracy and nearly approaches this minimum bound. Moreover, normalized mean square error (NMSE) is selected as the evaluation metrics for estimation accuracy. Finally, simulation results reveal a notable reduction in the estimation error of the proposed algorithm when compared to the baseline algorithms, confirming its estimation advantage.

发表机构

• Xi’an Jiaotong University（西安交通大学）
• Stevens Institute of Technology（史蒂文斯理工学院）
• Nanjing University of Science and Technology（南京理工大学）
• Beijing University of Posts and Telecommunications（北京邮电大学）
• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。