Foundations of User-Centric Cell-Free Massive MIMO
TL;DR: 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.
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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
Unknown Interference Modeling for Rate Adaptation in Cell-Free Massive MIMO Networks
Mahmoud Zaher,Emil Björnson,Marina Petrova +2 more
- 21 Apr 2024
TL;DR: This paper models unknown interference power in cell-free massive MIMO networks, enabling effective rate adaptation with guaranteed target outage, by proposing a method that accurately describes the distribution of interference power arising from scheduling variations in neighboring clusters.
The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems
L. Iliadis,Zaharias D. Zaharis,Sotirios P. Sotiroudis,Panagiotis Sarigiannidis,George K. Karagiannidis,Sotirios K. Goudos +5 more
TL;DR: A review of the state-of-the-art DL methods applied to CF MIMO communications systems is provided in this paper , along with the basic characteristics of cell-free networks.
WMMSE Beamforming for User-Centric Cell-Free Networks with Non-Coherent Joint Transmission
Xi Wang,Xiaotong Zhao,Juncheng Wang,Qingjiang Shi +3 more
- 04 Dec 2023
TL;DR: This work for the first time demonstrates the applicability of the WMMSE approach for NCJT in cell-free networks and proposes an efficient WMMSE based beamforming algorithm that is guaranteed to converge to a stationary point of the WSR maximization problem.
Methodologic Assessment of Beamforming Techniques for Interference Mitigation on GNSS Handheld Devices
06 Jun 2023
TL;DR: In this article , the authors proposed a methodologic approach for the performance assessment of beamforming techniques for interference mitigation on GNSS receivers, focusing on the effective C/N and phase and code jitters that can be expected after beamforming, which give a better assessment than simply taking into account indicators such as the interference null depth.
Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels
TL;DR: The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity and show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes.
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