Massive MIMO Has Unlimited Capacity

By Emil Björnson, Jakob Hoydis, Luca Sanguinetti. IEEE Transactions on Wireless Communications, 2017

396 citations recorded by the source corpus.

The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference caused by pilot contamination has been believed to create a finite capacity limit, as the number of antennas goes to infinity. In this paper, we prove that this is incorrect and an artifact from using simplistic channel models and suboptimal precoding/combining schemes. We show that with multicell MMSE precoding/combining and a tiny amount of spatial channel correlation or large-scale fading variations over the array, the capacity increases without bound as the number of antennas increases, even under pilot contamination. More precisely, the result holds when the channel covariance matrices of the contaminating users are asymptotically linearly independent, which is generally the case. If also the diagonals of the covariance matrices are linearly independent, it is sufficient to know these diagonals (and not the full covariance matrices) to achieve an unlimited asymptotic capacity.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/72e93217cb7fbd3a089d3de09e386c2113f72cd3

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Citations in the corpus

Foundations of User-Centric Cell-Free Massive MIMO (2020, methodology): “It has later been established that more advanced linear signal processing methods, such as minimum mean-squared error (MMSE) processing, are needed to make efficient use of Massive MIMO [23], [76], [124], [156].”
Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination (2019, background): “A downlink counterpart to Theorem 6 can also be obtained [20].”
Massive MIMO Detection Techniques: A Survey (2019, methodology): “It is approved that the capacity increases without a bound as the number of antenna increases, even under a pilot contamination, when multicell minimum mean square error (MMSE) precoding / combining and spatial channel correlation are used [22].”
Prospective Multiple Antenna Technologies for Beyond 5G (2019, background): “While pilot contamination might be less of a problem for ground-to-ground than initially believed [138], it is a major concern in MIMO-UAV communications.”
Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces (2020, background): “is that the linear scaling holds true even in the asymptotic regime where N → ∞ [3], [11], [12], [34]–[37], which has been utilized to define properties such as channel hardening and favorable propagation, as well as studying the fundamental impact of pilot contamination.”
Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation (2019, methodology): “Signal processing methods, such as minimum mean-squared error (MMSE) combining in the uplink, can be used individually at each BS to suppress interference from both the same and other cells [3], [9], without the need for any BS cooperation.”
Scalable Cell-Free Massive MIMO Systems (2019, background): “4.2], which has received particular attention in the literature since it might increase linearly with N , just as the signal term [22], [40], [41].”
Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays (2019, methodology): “4(b) per cell, if MMSE processing methods are used to cancel inter-cell interference [27].”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “Hence, we must establish the implied trade-off through a robust study for the effect of distributing (or co-locating) the available antennas on”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “Simulation results show that the total data rate obtained by the deep learning scheme is close to that obtained by the optimal user association scheme from [171].”