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arXiv 2108.02541cs.ITeess.SPmath.IT
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以用户为中心的无小区大规模MIMO的基础

Foundations of User-Centric Cell-Free Massive MIMO

Özlem Tuğfe Demir, Emil Björnson, Luca Sanguinetti

AI总结:

本专著系统阐述了以用户为中心的无小区大规模MIMO的基础,涵盖动机、数学定义、信道估计与收发信号处理算法、频谱效率分析及资源分配方法,揭示了性能、复杂度与前传需求的权衡。

摘要:

设想一个覆盖区域,其中每个移动设备与(众多接入点中)基于其需求选出的一组优选无线接入点进行通信,这些接入点通过协作联合为其提供服务,而非创建自治小区。这有效地引出了一种以用户为中心的后蜂窝网络架构,能够解决蜂窝网络中出现的许多干扰问题和服务质量波动。该概念被称为以用户为中心的无小区大规模MIMO(多输入多输出),其根源在于三项技术组件的交叉:Massive MIMO、协作多点处理和超密集网络。主要挑战在于以实际可行的方式实现无小区运行的优势,使计算复杂度和前传需求具备可扩展性,以支持包含大量移动设备的超大规模网络。本专著涵盖了以用户为中心的无小区大规模MIMO的基础,从动机和数学定义出发。接着描述了用于信道估计、上行数据接收和下行数据传输的最先进信号处理算法,涵盖集中式或分布式实现。可达频谱效率通过一个贯穿全文的示例进行了数学推导和数值评估,该示例揭示了各种系统参数和算法选择的影响。对通信性能、计算复杂度和前传信令需求之间的基本权衡进行了深入分析。最后,提供了用于导频分配、动态协作簇形成和功率优化的基本算法,并回顾了与这些及其他资源分配问题相关的开放性问题。所有数值示例均可使用随附的Matlab代码复现。

英文摘要:

Imagine a coverage area where each mobile device is communicating with a preferred set of wireless access points (among many) that are selected based on its needs and cooperate to jointly serve it, instead of creating autonomous cells. This effectively leads to a user-centric post-cellular network architecture, which can resolve many of the interference issues and service-quality variations that appear in cellular networks. This concept is called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and has its roots in the intersection between three technology components: Massive MIMO, coordinated multipoint processing, and ultra-dense networks. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to enable massively large networks with many mobile devices. This monograph covers the foundations of User-centric Cell-free Massive MIMO, starting from the motivation and mathematical definition. It continues by describing the state-of-the-art signal processing algorithms for channel estimation, uplink data reception, and downlink data transmission with either centralized or distributed implementation. The achievable spectral efficiency is mathematically derived and evaluated numerically using a running example that exposes the impact of various system parameters and algorithmic choices. The fundamental tradeoffs between communication performance, computational complexity, and fronthaul signaling requirements are thoroughly analyzed. Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster formation, and power optimization are provided, while open problems related to these and other resource allocation problems are reviewed. All the numerical examples can be reproduced using the accompanying Matlab code.

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