Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination

By Luca Sanguinetti, Emil Björnson, Jakob Hoydis. IEEE Transactions on Communications, 2019

349 citations recorded by the source corpus.

Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted into the 5G New Radio Standard and base stations (BSs) with $M=64$ fully digital transceivers have been commercially deployed in sub-6GHz bands. The fast progress was enabled by many solid research contributions of which the vast majority assume spatially uncorrelated channels and signal processing schemes developed for single-cell operation. These assumptions make the performance analysis and optimization of Massive MIMO tractable but have three major caveats: 1) practical channels are spatially correlated; 2) large performance gains can be obtained by multicell processing, without BS cooperation; 3) the interference caused by pilot contamination creates a finite capacity limit, as $M\to \infty $ . There is a thin line of papers that avoided these caveats, but the results are easily missed. Hence, this tutorial article explains the importance of considering spatial channel correlation and using signal processing schemes designed for multicell networks. We present recent results on the fundamental limits of Massive MIMO, which are not determined by pilot contamination but the ability to acquire channel statistics. These results will guide the journey towards the next level of Massive MIMO, which we call “Massive MIMO 2.0”.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bdcb9bd14936572e60e1e1eaa82e9fe02ec8b7b5

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Citations in the corpus

Prospective Multiple Antenna Technologies for Beyond 5G (2019, background): “This virtual approach allows operators and manufacturers to deploy sophisticated precoding schemes and easily upgrade to new precoding schemes because the user is not required to have any knowledge of W 1 [1] , . . . , W 1 [ K ] .”
Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces (2020, background): “, ≥ 64) to deliver large array gains and perform spatial multiplexing of many users on the same time-frequency resource [3]–[5].”
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (2019, background): “Large Intelligent Surfaces (LISs) are envisioned as intrinsic components of beyond-5G wireless systems [1]–[10].”
Scalable Cell-Free Massive MIMO Systems (2019, methodology): “Now that Massive MIMO is a rather mature technology [2], [23], [24] that has made its way into the 5G standard [25], the research focus is shifting back to Network MIMO, but under the new name Cell-Free Massive MIMO coined in [5], [6].”
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities (2020, background): “on of the pilot signal spacing in time and frequency domain, exploitation of limited angular spread of the channel [97,98], and advanced signal processing methods for reduction of pilot contamination [99,100]. A related problem is the quantization and feedback of CSI. While reciprocity calibration, ”
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].”