arXiv：cs.LG（机器学习，全量分类）· Melih Can Zerin·· 13 小时前AI 评分26
均值空间频率解耦：FDD 大规模 MIMO 中基于学习的上行到下行协方差转换
Mean Spatial Frequency Decoupling for Learning-Based Uplink-to-Downlink Covariance Conversion in FDD Massive MIMO
AI 导读
针对 FDD 大规模 MIMO 中上行到下行信道协方差矩阵转换的学习方法随天线数增加精度下降的问题，该论文提出 deramping 方案：从上行 CCM 单独估计均值 AoA 引起的相位斜坡斜率，并以闭式映射到下行频段，使学习器只需处理对均值 AoA 基本不敏感的残差。
正文
Abstract: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.
| Subjects: | Signal Processing (eess.SP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00596 [eess.SP] |
| (or arXiv:2610.00596v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00596 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Melih Can Zerin [view email]
[v1]
Wed, 30 Sep 2026 19:00:12 UTC (105 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org