发表机构
Middle East Technical University（中东技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对FDD大规模MIMO中基于学习的UL-DL协方差转换随天线数增加性能退化的问题，提出均值空间频率解耦（去斜坡）方法，通过闭式映射平均AoA相位斜坡，显著降低估计误差并改善下行信道估计。
AI 中文摘要
在频分双工（FDD）大规模多输入多输出（MIMO）系统中，研究了上行（UL）到下行（DL）信道协方差矩阵（CCM）转换问题，以减轻信道估计所需的DL训练和反馈的沉重负担。基于学习的方法在一定的阵列规模内表现良好，但对于固定的数据集规模，其精度随天线数量的增加而下降，以至于简单的基于模型的方法优于它们。本文识别了这种行为的一个关键原因并加以解决。平均到达角（AoA）在CCM的滞后项上引起相位斜坡。由于该斜坡的振荡速率随天线数量增加而增长，固定规模的数据集相对于必须捕获的变化变得越来越稀疏。我们提出从UL CCM中单独估计该斜坡的斜率，并以闭式形式将其映射到DL频带，使学习器处理对平均AoA基本不敏感的残差，从而显著减少随天线数量增加的性能退化。所提出的方案称为去斜坡（deramping），是预处理和后处理步骤的组合，适用于基于学习的转换方法而不改变其内部结构，这在三个结构不同的学习器上得到了验证。仿真结果表明，去斜坡在均匀、拉普拉斯和高斯角功率谱下降低了所有三个学习器的协方差估计误差，使基于插值的学习器在大阵列规模下保持领先于基于模型的基准，并改善了下行信道估计。
英文摘要
In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, the uplink (UL)-to-downlink (DL) channel covariance matrix (CCM) conversion problem is studied to relieve the heavy burden of DL training and feedback required for channel estimation. Learning- based methods perform well up to a certain array size, but for a fixed dataset size their accuracy deteriorates with the number of antennas, to the point where simple model-based methods outperform them. This paper identifies a key cause of this behavior and addresses it. The mean angle of arrival (AoA) induces a phase ramp along the lags of the CCM. Since the oscillation rate of this ramp grows with the number of antennas, a dataset of fixed size becomes increasingly sparse relative to the variation that must be captured. We propose estimating the slope of this ramp from the UL CCM separately and mapping it to the DL band in closed form, leaving the learner with a residual that is largely insensitive to the mean AoA, which substantially reduces the performance degradation with an increasing number of antennas. The proposed scheme, termed deramping, is a combination of pre- and post-processing steps that applies to learning-based conversion methods without altering their internal structure, as demonstrated on three structurally different learners. Simulation results show that deramping reduces the covariance estimation error of all three learners under uniform, Laplacian, and Gaussian angular power spectra,keeps the interpolation-based learners ahead of a model-based benchmark at large array sizes, and improves downlink channel estimation.
