Foundations of User-Centric Cell-Free Massive MIMO
Foundations of User-Centric Cell-Free Massive MIMO
复制标题
DOI：
10.1561/2000000109
复制
发表时间：
2020-01-01
影响因子：
--
通讯作者：
Sanguinetti, Luca
中科院分区：
文献类型：
--
作者：
Demir, Ozlem Tugfe;Bjornson, Emil;Sanguinetti, Luca
设想一个覆盖区域，其中每个移动终端与一组优选的无线接入点（在许多无线接入点中）进行通信，这些无线接入点是基于其需要选择的，并且进行协作以共同为其服务，而不是创建自主小区。这有效地导致了以用户为中心的后蜂窝网络架构，其可以解决蜂窝网络中出现的许多干扰问题和服务质量变化。这个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），其根源在于三个技术组件之间的交叉：大规模MIMO，协调多点处理和超密集网络。主要挑战是以实际可行的方式实现无小区操作的益处，其中计算复杂性和前传要求是可扩展的，以实现具有许多移动的设备的大型网络。本专著涵盖了以用户为中心的无小区大规模MIMO的基础，从动机和数学定义开始。它继续描述用于信道估计、上行链路数据接收和下行链路数据传输的最先进的信号处理算法，其具有集中式或分布式实现。可实现的频谱效率的数学推导和数值评估使用一个运行的例子，暴露了各种系统参数和算法选择的影响。通信性能，计算复杂度和前传信令要求之间的基本权衡进行了彻底的分析。最后，导频分配的基本算法，动态合作集群的形成，和功率优化，而开放的问题，这些和其他资源分配问题进行审查。所有的数值例子都可以使用附带的Matlab代码重现。
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 Cellfree 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.