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
Wideband Cell-Free mmWave Massive MIMO-OFDM: Beam Squint-Aware Channel Covariance-Based Hybrid Beamforming
TL;DR: Numerical results show that beam squint-aware designs outperform the beam Squint-unaware strategies, specially in typical wideband cell free mmWave massive MIMO-OFDM scenarios where a combination of a very high carrier frequency, a large system bandwidth, and a large-scale antenna array causes the spatial-wideband effect.
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The Impact of Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks
Fabian Gottsch,Noboru Osawa,Takeo Ohseki,Kosuke Yamazaki,Giuseppe Caire +4 more
- 27 Sep 2021
TL;DR: In this article, the authors considered a scalable user-centric wireless network with dynamic cluster formation as defined by Bjornsson and Sanguinetti, and defined the ideal performance based on ideal but partial CSI, i.e., the CSI that can be estimated based on the users to antenna heads cluster connectivity.
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Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources
Özlem Tuğfe Demir,Meysam Masoudi,Emil Björnson,Çiçek Çavdar +3 more
TL;DR: Energy-aware joint orchestration of cloud, fronthaul, and radio resources in cell-free massive MIMO over O-RAN for improved energy efficiency.
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Uplink-Downlink Duality and Precoding Strategies with Partial CSI in Cell-Free Wireless Networks
Fabian Gottsch,Noboru Osawa,Takeo Ohseki,Kosuke Yamazaki,Giuseppe Caire +4 more
- 13 Jan 2022
TL;DR: A new duality method achieves almost symmetric "optimistic ergodic rates" for UL and DL while saving considerable computational complexity since the UL combining vectors are reused as DL precoders.
Multi-Agent Reinforcement Learning for Distributed Resource Allocation in Cell-Free Massive MIMO-enabled Mobile Edge Computing Network
TL;DR: In this paper , a joint communication and computing resource allocation problem was formulated for a cell-free massive MIMO-enabled mobile edge computing (MEC) network with the objective of minimizing the total energy consumption of the users while meeting the ultra-low delay constraints.
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