SpecEES: Collaborative Research: Stochastic Geometry Meets Channel Measurements: Comprehensive Modeling, Analysis,Fundamental Design-tradeoffs in Real-world Massive-MIMO Networks

处理不断增长的无线容量需求的一个有希望的解决方案是在基站部署比小区中的用户数量多得多的天线单元。这种所谓的大规模MIMO方法创造了额外的自由度，可以用来“整形”波束，从而提高通信链路的能量和频谱效率。大规模MIMO可以通过多种方式实现。一种极端情况是集中式实施，其中数百个天线部署在小区内的单个位置。另一个极端是完全分布式实现，其中分布在整个小区的数百个单天线远程射频头连接到一个公共基带处理单元，从而形成一个分布式基站。介于这两个极端之间的是相对较少研究的半分布式实现的情况，其中多天线远程无线电头分布在整个小区中。虽然大规模MIMO的想法已经存在了几年，但我们对这些系统的性能的了解仍然有限。这主要是由于缺乏真实世界的传播模型(特别是对于半分布式实现)以及可以揭示用户和基站的不同空间分布的性能趋势的数学工具，这两者都已知会显著影响这些系统的性能。这项研究的主要目标是开发一种变革性的测量驱动的分析方法，以实现大规模MIMO网络的高效部署，最终通过提高频谱效率来带来更好的客户体验，并通过提高能源效率来实现更环保的无线通信。该项目的所有主要成果将通过出版物、教程和行业合作广泛传播。该研究将通过融合通信理论、随机几何、点过程理论和传播建模等多个学科的思想，开发一种全面的测量驱动的方法来分析大规模MIMO系统的频谱和能量效率，产生以下关键创新：(1)随机几何感知的频谱和能量效率度量，以促进不同类型的大规模MIMO系统的基本分析和公平比较；(2)新的随机几何方法，用于集中式、分布式和半分布式大规模MIMO系统的覆盖分析；(3)不同类型传播模型下分布式大规模MIMO系统的信道特征的新结果，(4)使用独一无二的信道探测仪的大规模测量活动(为大规模MIMO系统量身定做)，(5)用于半确定性大规模MIMO设置的全新的信道模型，其除了传统建模的从用户到不同RRH的阴影相关性之外，还将包括路径损耗和色散度量的相关性，以及(6)用于毫米波频率的新的大规模MIMO信道模型。因此，该项目将结合来自随机几何的强大的空间建模工具与真实世界的海量MIMO信道模型来比较和对比不同风格的海量MIMO在真实世界操作约束下的性能，这将直接影响未来蜂窝网络的设计、运营和管理。

One promising solution to handle the ever-increasing demand for wireless capacity is to deploy significantly higher number of antenna elements at the base station compared to the number of users in the cell. This so called Massive MIMO approach creates extra degrees of freedom that can be used to "shape" beams, thus enhancing both the energy and spectral efficiency of communication links. Massive MIMO can be implemented in a variety of ways. One extreme is the concentrated implementation in which hundreds of antennas are deployed at a single location within a cell. On the other extreme is the fully distributed implementation in which hundreds of single-antenna remote radio heads, distributed throughout the cell, are connected to a common baseband processing unit, thus forming a distributed base station. In between these extremes is the relatively less-investigated case of semi-distributed implementation in which multi-antenna remote radio heads are distributed across the cell. While the massive MIMO idea has been around for several years now, our understanding of the performance of these systems is still limited. This is mainly due to the lack of real-world propagation models (especially for the semi-distributed implementations) as well as mathematical tools that can expose performance trends for different spatial distributions of the users and base stations, both of which are known to significantly impact the performance of these systems. The main goal of this research is to develop a transformative measurements-driven analytical approach to enable the efficient deployment of massive MIMO networks ultimately leading to better customer experience, via improved spectral efficiency, and greener wireless communications, via improved energy efficiency. All the key outcomes of this project will be widely disseminated through publications, tutorials, and industry collaborations. The proposed research will develop a comprehensive measurement-driven approach to the spectral and energy efficiency analyses of massive MIMO systems by blending ideas from multiple disciplines, such as communication theory, stochastic geometry, point process theory, and propagation modeling, yielding the following key innovations: (1) stochastic geometry-aware spectral and energy efficiency metrics to facilitate fundamental analysis and fair comparison across different flavors of massive MIMO, (2) new stochastic geometry approaches to the coverage analysis of concentrated, distributed, and semi-distributed massive MIMO systems, (3) new results on the channel characterization for distributed massive MIMO systems under different classes of propagation models, (4) extensive measurement campaign (tailored to massive MIMO systems) using a one-of-its-kind channel sounder, (5) fundamentally new channel models for semi-deterministic massive MIMO setups that will incorporate correlation of the pathloss and dispersion metrics, in addition to traditionally modeled correlation of the shadowing from user to different RRHs, and (6) new massive-MIMO channel models for the millimeter-wave frequencies. This project will thus combine the powerful spatial modeling tools from stochastic geometry with real-world massive MIMO channel models to compare and contrast the performance of different flavors of massive MIMO under real-world operational constraints, which will directly impact the design, operation, and management of future cellular networks.