Cell-Free Massive MIMO: A Survey

Elhoushy, Salah; Ibrahim, Mohamed; Hamouda, Walaa

doi:10.1109/comst.2021.3123267

Cited by 187 publications

(82 citation statements)

References 178 publications

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“…The extreme case of distributed massive MIMO is called cell-free (CF) massive MIMO, where all antennas are distributed over the region and connected to a central processing unit, essentially removing the notion of cells. Spatially dispersed antennas are required to share data and frequency and timing information, making CF networks not scalable in practice [2]. To address these challenges, Radioweavesas been introduced in [3].…”

Section: Introduction -From 5g To Radioweavesmentioning

confidence: 99%

Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning

Callebaut¹,

Mulders²,

Ottoy³

et al. 2022

Preprint

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New concepts for next-generation wireless systems are being developed. It is expected that these 6G and beyond systems will incorporate more than only communication, but also sensing, positioning, (deep) edge computing, and other services. The discussed measurement facility and approach, named Techtile, is an open, both in design and operation, and unique testbed to evaluate these newly envisioned systems. Techtile is a multi-functional and versatile testbed, providing finegrained distributed resources for new communication, positioning and sensing technologies. The facility enables experimental research on hyperconnected interactive environments and validation of new algorithms and topologies. The backbone connects 140 resource units equipped with edge computing devices, software-defined radios (SDRs), sensors, and LED sources. By doing so, different network topologies and local-versuscentral computing can be assessed. The introduced diversity of i) the technologies (e.g., RF, acoustics and light), ii) the distributed resources and iii) the interconnectivity allows exploring more degrees and new types of diversity, which can be investigated in this testbed.

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Section: Introduction -From 5g To Radioweavesmentioning

confidence: 99%

Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning

Callebaut¹,

Mulders²,

Ottoy³

et al. 2022

Preprint

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show abstract

“…However, to date, although there have already been a large number of work on CF mMIMO [3] and IBFD systems [4], few research considers the combination of these two techniques. One of the underlying reasons is that IBFD-based CF (IBFD-CF) systems suffer from the severe self-interference (SI) and cross-link interference (CLI) problems.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Li¹,

Yang²,

Maunder³

et al. 2022

Preprint

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In-band full duplex-based cell-free (IBFD-CF) systems suffer from severe interference problem including self-interference (SI) and cross-link interference (CLI), especially when cell-free (CF) systems are operated in a distributed way. To this end, we propose multicarrier-division duplex (MDD) as an enabler for full-duplex (FD)-style operation in distributed CF massive MIMO systems, where DL and UL transmissions take place simultaneously at the same frequency band but mutually orthogonal subcarrier sets. In order to maximize the spectral efficiency (SE) in the proposed systems, we present heterogeneous graph neural network specific for CF systems (CF-HGNN), which consists of an adaptive node embedding layer, meta-path based message passing, meta-path based attention and downstream power allocation learning. In particular, the adaptive node embedding layer can handle the varying number of access points (APs), mobile stations (MSs) and subcarriers, and the involved attention mechanism enables each AP/MS node in CF-HGNN to aggregate the information from interfering path and communication path with different priorities. Numerical results show that CF-HGNN is capable of using 10 4 times less operation time to achieve the 99% performance of the SE of quadratic transform and successive convex approximation (QT-SCA). Additionally, CF-HGNN also significantly outperforms unfair greedy method in terms of SE performance. Furthermore, CF-HGNN exhibits good adaptivity to varying number of nodes and subcarriers, and also generalization ability to different sizes of CF network.

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“…This issue can be solved by effectively implementing mm-Wave technology with massive MIMO systems employing large antenna arrays with convenient beamforming techniques to mitigate the effect of path loss [2,3]. Furthermore, cell-free systems are accomplished by deploying many geographically distributed access points (APs) over the coverage area without cell boundaries [4]. These APs are linked to a central processing unit (CPU) via high-speed fibre or wireless backhaul/fronthaul links to simultaneously serve user equipment (UE) and time-frequency resources [5].…”

Section: Introductionmentioning

confidence: 99%

Antenna Selection Based on Matching Theory for Uplink Cell-Free Millimetre Wave Massive Multiple Input Multiple Output Systems

Ayidh

Sambo

Olaosebikan

et al. 2022

Telecom

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In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.

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Cell-Free Massive MIMO: A Survey

Cited by 187 publications

References 178 publications

Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning

Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Antenna Selection Based on Matching Theory for Uplink Cell-Free Millimetre Wave Massive Multiple Input Multiple Output Systems

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