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Foundations of User-Centric Cell-Free Massive MIMO

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TLDR
In this article, the fundamental tradeoffs between communication performance, computational complexity, and fronthaul signaling requirements are thoroughly analyzed, while open problems related to these and other resource allocation problems are reviewed.
Abstract
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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Citations
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Journal ArticleDOI

Rate-Splitting Assisted Massive Machine-Type Communications in Cell-Free Massive MIMO

TL;DR: Numerical results show that RSMA effectively mitigates the effect of pilot contamination in the downlink and achieves a significant performance gain over a conventional cell-free massive MIMO network.
Journal ArticleDOI

A survey on user-centric cell-free massive MIMO systems

TL;DR: A survey of the state-of-the-art literature on cell-free mMIMO is provided in this article , where the authors highlight the significance and the basic properties of CF-MIMO and highlight the key lessons learned in this field.
Journal ArticleDOI

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

TL;DR: In this article , the authors provide a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G, and a critical appraisal of the network architecture and key technologies is presented.
Journal ArticleDOI

Full-Duplex Cell-Free Massive MIMO Systems: Analysis and Decentralized Optimization

TL;DR: It is shown that low fronthaul capacity reduces the number of users each AP can support, and the cell-free system, consequently, becomes user-centric and the utility of the WSEE metric to incorporate heterogeneous EE requirements of users is demonstrated.
Journal ArticleDOI

On channel estimation in cell-free massive MIMO for spatially correlated channels with correlated shadowing under Rician fading

TL;DR: This work proposes partial CE for each user because only APs with the best channel conditions are allowed to compute channel estimates, and introduces a dynamic cooperation cluster framework in which the user is not served with the whole network but only the APs that present thebest channel conditions regarding that user.
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