arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.16459cs.ITeess.SPmath.IT
收藏

连续流体天线采样用于无细胞大规模MIMO中的信道估计

Continuous Fluid Antenna Sampling for Channel Estimation in Cell-Free Massive MIMO

Aalto University(阿尔托大学);University of Granada(格拉纳达大学);Universidad de Valladolid(巴利亚多利德大学);University College London(伦敦大学学院);Yonsei University(延世大学)

Masoud Kaveh, Farshad Rostami Ghadi, Francisco Hernando-Gallego, Diego Martin, Riku Jantti, Kai-Kit Wong

AI总结:

本文提出连续流体天线采样方法,用于无细胞大规模MIMO中的信道估计,通过高斯过程回归实现更精确的估计性能。

摘要:

在本信函中,我们开发了一个连续流体天线(FA)框架,用于无细胞大规模多输入多输出(CF-mMIMO)系统中的上行链路信道估计。通过将无线信道建模为空间相关高斯随机场,信道估计被公式化为一个高斯过程(GP)回归问题,具有运动约束的空间采样。推导了线性最小均方误差(LMMSE)估计器及其相应估计误差的闭式表达式。在相同位置约束下,与离散端口架构进行了根本性的比较,表明连续FA采样在任何有限的试点预算下都能实现等于或更低的估计误差,对于非退化的空间相关模型有严格的优势。数值结果验证了分析并展示了连续FA采样相对于离散基线的性能提升。

英文摘要:

In this letter, we develop a continuous fluid antenna (FA) framework for uplink channel estimation in cell-free massive multiple-input and multiple-output (CF-mMIMO) systems. By modeling the wireless channel as a spatially correlated Gaussian random field, channel estimation is formulated as a Gaussian process (GP) regression problem with motion-constrained spatial sampling. Closed-form expressions for the linear minimum mean squared error (LMMSE) estimator and the corresponding estimation error are derived. A fundamental comparison with discrete port-based architectures is established under identical position constraints, showing that continuous FA sampling achieves equal or lower estimation error for any finite pilot budget, with strict improvement for non-degenerate spatial correlation models. Numerical results validate the analysis and show the performance gains of continuous FA sampling over discrete baselines.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑