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Foundations of User-Centric Cell-Free Massive MIMO
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering. KTH Royal Inst Technol, Sweden.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering. KTH Royal Inst Technol, Sweden.ORCID iD: 0000-0002-5954-434X
Univ Pisa, Italy.
2020 (English)In: FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING, ISSN 1932-8346, Vol. 14, no 3-4, p. 162-472Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
NOW PUBLISHERS INC , 2020. Vol. 14, no 3-4, p. 162-472
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-173526DOI: 10.1561/2000000109ISI: 000613682400001OAI: oai:DiVA.org:liu-173526DiVA, id: diva2:1530117
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; Excellence Center at Linkoping -Lund in Information Technology (ELLIIT); Center for Industrial Information Technology (CENIIT); Swedish Research CouncilSwedish Research Council; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research; University of Pisa under the PRA Research Project CONCEPT; Italian Ministry of Education and Research (MIUR)Ministry of Education, Universities and Research (MIUR)

Available from: 2021-02-21 Created: 2021-02-21 Last updated: 2021-02-21

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Tugfe Demir, Özlem TugfeBjörnson, Emil
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