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arXiv 2408.04484eess.SPcs.AI
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基于二阶统计量的MU-MIMO无线用户聚类统计框架

Statistical Framework for Clustering MU-MIMO Wireless via Second Order Statistics

Roberto Pereira, Xavier Mestre

AI总结:

本文针对MU-MIMO无线用户聚类问题,基于正定矩阵黎曼流形上的对数欧几里得距离,提出一种样本协方差矩阵距离的一致估计器,并通过中心极限定理建立其渐近高斯性,从而为现实条件下聚类算法性能预测提供统计框架。

摘要:

本工作通过考察用户信道协方差矩阵之间的距离来探索无线用户聚类,这些矩阵位于正定矩阵的黎曼流形上。具体而言,我们考虑一种多个样本协方差矩阵(SCM)之间对数欧几里得距离的估计器,该估计器在样本数与观测规模以相同速率无界增长时具有一致性。在多用户MIMO(MU-MIMO)无线通信系统的背景下,我们开发了一个统计框架,能够在现实条件下准确预测聚类算法的性能。具体来说,我们提出了一个中心极限定理,建立了在两个样本协方差矩阵上计算的对数欧几里得距离一致估计器的渐近高斯性。

英文摘要:

This work explores the clustering of wireless users by examining the distances between their channel covariance matrices, which reside on the Riemannian manifold of positive definite matrices. Specifically, we consider an estimator of the Log-Euclidean distance between multiple sample covariance matrices (SCMs) consistent when the number of samples and the observation size grow unbounded at the same rate. Within the context of multi-user MIMO (MU-MIMO) wireless communication systems, we develop a statistical framework that allows to accurate predictions of the clustering algorithm's performance under realistic conditions. Specifically, we present a central limit theorem that establishes the asymptotic Gaussianity of the consistent estimator of the log-Euclidean distance computed over two sample covariance matrices.

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。

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