编织与征服：密集天线部署的测量分析
Weave and Conquer: A Measurement-based Analysis of Dense Antenna Deployments
摘要：
大规模多输入多输出在室外宏小区的背景下带来显著的性能改进，例如有利的传播条件、空间受限的通信、克服路径损耗的高天线增益以及良好的角度定位。在本文中，我们将探讨这些好处如何扩展到室内散射丰富的部署密集的室内测量大规模多输入多输出数据集的基础上。首先，我们设计和实现三种不同的和相关的拓扑结构，以定位我们的64个天线的环境中：\emph{Massive MIMO, RadioStripes}和\emph{RadioWeaves}拓扑结构。第二，我们测量252004室内通道的$3x3m^2$区域的每个拓扑结构，使用自动化的用户定位和测量系统。使用这个密集的数据集，我们提供了一个独特的分析系统级的属性，如路径损耗，有利的传播，空间聚焦和本地化性能。我们基于测量的分析验证并量化了将天线分布在整个环境中会提高传播公平性，改善有利的传播条件，提高空间限制，最终实现高定位性能。该数据集是公开可用的，并且可以用作未来室内通信系统和通信模型的基准测试的参考数据库。我们概述了我们观察到的实施挑战，并列出了可以从使用此数据集作为基准中受益的各种研发挑战。
英文摘要：
Massive MIMO is bringing significant performance improvements in the context of outdoor macrocells, such as favourable propagation conditions, spatially confined communication, high antenna gains to overcome pathloss, and good angular localisation. In this paper we explore how these benefits scale to indoor scattering-rich deployments based on a dense indoor measured Massive MIMO dataset. First, we design and implement three different and relevant topologies to position our 64 antennas in the environment: \emph{Massive MIMO, RadioStripes} and \emph{RadioWeaves} topologies. Second, we measure 252004 indoor channels for a $3x3m^2$ area for each topology, using an automated user-positioning and measurement system. Using this dense dataset, we provide a unique analysis of system level properties such as pathloss, favourable propagation, spatial focusing and localisation performance. Our measurement-based analyses verify and quantify that distributing the antennas throughout the environment results in an improved propagation fairness, better favourable propagation conditions, higher spatial confinement and finally a high localisation performance. The dataset is publicly available and can serve as a reference database for benchmarking of future indoor communication systems and communication models. We outline the implementation challenges we observed, and also list diverse R\&D challenges that can benefit from using this dataset as a benchmark.
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。
