Cell-Free Massive MIMO Versus Small Cells

By Hien Quoc Ngo, Alexei E. Ashikhmin, Hong Yang, Erik G. Larsson, T. Marzetta. IEEE Transactions on Wireless Communications, 2016

2,606 citations recorded by the source corpus.

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/99bc136f50581fcf6bbc35356adadb1e1d751565

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Citations in the corpus

User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, methodology): “We also explore questions about future research directions, and we contemplate some of the challenges facing deployment.”
Precoding and Power Optimization in Cell-Free Massive MIMO Systems (2017, methodology): “In [21] and [22], performance of conjugate beamforming (CB) in cell-free systems has been investigated with emphasis on pilot assignment algorithms to mitigate pilot contamination effect.”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “A reported study in [158] has demonstrated that the cell-free massive MIMO scheme improves 95%-likely per-user throughput by ﬁve times and by ten times under correlated shadow fading, with respect to the small-cell solu-tion.”
Channel Hardening and Favorable Propagation in Cell-Free Massive MIMO With Stochastic Geometry (2017, methodology): “which was used for CF Massive MIMO in [11].”
A Joint Precoding Framework for Wideband Reconfigurable Intelligent Surface-Aided Cell-Free Network (2020, background): “This promising technique has been considered as a potential candidate for future communication systems [9], and has attracted the increasing research interest such as resource allocation [10], precoding/beamforming [11], channel estimation [12] in recent years.”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, background): “At the network level, cell-free massive multipleinput multiple-output (MIMO) has been proposed as a promising architecture towards user-centric networks [9]–[11], which blurs the traditional cell boundaries and may provide”
5G-Advanced Toward 6G: Past, Present, and Future (2023, methodology): “To address this issue, architectures like distributed massive MIMO and cell-free massive MIMO [47], [48], coordinated multipoint (also known as network MIMO), and distributed antenna systems have been studied.”
Foundations of User-Centric Cell-Free Massive MIMO (2020, methodology): “4, was analyzed in the early works [121], [126] with the focus on a distributed operation where the APs perform all the signal processing tasks, except for those that critically require central coordination.”
A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications (2023, methodology): “To cope with this problem, a distributed mMIMO paradigm called cell-free massive MIMO (CF mMIMO) has been proposed in [37].”
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities (2020, neutral): “, [132–139] and references therein.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “[46], [47] and reconﬁgurable intelligent surface (RIS) [48], [49]) to support fast exchange for high-dimensional model updates, and next-generation network architectures (e.g., space-air-ground integrated network (SAGIN) [50], [51]) to support diverse edge learning models and topologies.”
Learning the MMSE Channel Estimator (2017, background): “A third example with a decomposition as in Assumption 2 is a setup with distributed antennas [18], [19].”
On the Total Energy Efficiency of Cell-Free Massive MIMO (2017, methodology): “More importantly, in a cell-free massive MIMO configuration, the service antennas are brought close to the users, which yields a high degree of macro-diversity and low path losses [7]; hence, many users can be served simultaneously with uniformly good quality-ofservice.”
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges (2023, background): “The cell-free massive MIMO architecture [110], [111], [175], on the other hand, may be a promising one in support of ISAC systems.”
Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination (2019, background): “However, the general theory behind Massive MIMO is not limited to that case, but has recently been applied also to cellfree networks [74]–[76].”
Cell-Free Massive MIMO: A New Next-Generation Paradigm (2019, methodology): “On the other hand, the optimization of the receiver filter coefficients at the CPU was considered in [38], which significantly improved the UL user rate.”