E. G. Larsson, F. Tufvesson, O. Edfors, and T. L. Marzetta, “Massive MIMO for Next Generation Wireless Systems,” IEEE Communications Magazine, vol. 52, no. 2, pp. 186-195, February 2014.

This paper provides an easy-to-read overview of massive MIMO technology.  It lists the potential benefits of massive MIMO, including a ten-fold capacity increase and hundred-fold energy efficiency improvement, the reduction of per-antenna power, simplified RF cabling, and a simplified MAC layer.  It also provides a list of factors that limit massive MIMO, including pilot contamination, imperfect channel reciprocity, as well as a list of open research problems, including the development of efficient hardware and signal-processing algorithms, better channel characterization, and prototyping.

L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An Overview of Massive MIMO: Benefits and Challenges,” IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 5, pp. 742-758, October 2014.

This paper provides a comprehensive overview of massive MIMO, starting from an information-theoretic perspective and proceeding to implementation issues.  Particular attention is paid to the pilot contamination problem.  Challenges and opportunities associated with implementing massive MIMO in future wireless systems are discussed.

E. Björnson, E. G. Larsson and T. Marzetta, “Massive MIMO: Ten Myths and One Critical Question", IEEE Communications Magazine, vol. 54, no. 2, pp. 114-123, February 2016.

This paper debunks ten common misconceptions about Massive MIMO and outlines the basic facts in the by-now classical FDD versus TDD debate.

T. Marzetta, “Massive MIMO: An Introduction,” Bell Labs Technical Journal, vol. 20, pp. 11-22, March 2015.

This paper is a tutorial introduction to massive MIMO by its originator.

K. Zheng, L. Zhao, J. Mei, B. Shao, W. Xiang, and L. Hanzo, “Survey of Large-Scale MIMO Systems,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1738-1760, third quarter 2015.

This paper provides a survey of large-scale MIMO, wherein there are hundreds of antennas serving dozens of users. Special attention is paid to working in three dimensions.  The paper covers measurements and modeling, applications, theoretical and measured performance with various precoders and receivers, and related technologies such as inter-cell interference coordination (ICIC).

“Special Issue on 5G Wireless Systems with Massive MIMO,” IEEE Systems Journal, vol. 11, no. 1, March 2017.

“Recent Advances in Massive MIMO Systems,” EURASIP Journal on Advances in Signal Processing, 2016.

“Special Issue on Large-Scale Multiple Antenna Wireless Systems,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, February 2013.

“Special Issue on Signal Processing for Large-Scale MIMO,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, No. 5, October 2014.

“Special Issue on Massive MIMO,” Journal of Communications and Networks (JCN), vol. 15, no. 4, August 2013.

J. G. Andrews, S. Buzzi, W. Choi, S. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, “What Will 5G Be?,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1065-1072, June 2014.

Massive MIMO is contemplated as one of several enabling technologies for emerging 5G systems.

V. Jungnickel, K. Manolakis, W. Zirwas, B. Panzner, V. Braun, M. Lossow, M. Sternad, R. Apelfröjd, and T. Svensson, “The Role of Small Cells, Coordinated Multipoint, and Massive MIMO in 5G,” IEEE Communications Magazine, vol. 52, no. 5, pp. 44-51, May 2014.

Massive MIMO is presented as one of three contributors to reaching higher spectral efficiencies, the others being advanced interference mitigation and densification using small cells.

F. Boccardi, R. W. Heath, Jr., A. Lozano, T. L. Marzetta, and P. Popovski, “Five Disruptive Technology Directions for 5G,” IEEE Communications Magazine, vol. 52, no. 2, pp. 74-80, Feb. 2014.

This article reviews five different research directions that may define 5G, including massive MIMO. It has a nice comparison of the target operating regimes in terms of data rate and latency for each technology.

H. Ji, Y. Kim, J. Lee, E. Onggosanusi, Y. Nam, J. Zhang, B. Lee and B. Shim, “ Overview of Full-Dimension MIMO in LTE-Advanced Pro,” IEEE Communications Magazine, vol. 55, no. 2, pp. 176-184, February 2017.

The paper reviews the application of massive MIMO to LTE-Advanced Pro cellular systems, where it is called "full-dimensional MIMO".  The article provides details of full-dimensional MIMO as considered in Release 13 of LTE, including challenges encountered by the standardization process.

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Introducing Facebook's New Terrestrial Connectivity Systems — Terragraph and Project ARIES

H. Huh, G. Caire, H. C. Papadopoulos, and S. A. Ramprashad, “Achieving ‘Massive MIMO’ Spectral Efficiency with a Not-So-Large Number of Antennas,” IEEE Transactions on Wireless Communications, vol. 11, no. 9, pp. 3226-3239, September 2012.

This paper proposes architectural improvements that allow the number of antennas per cell in a massive MIMO system to be reduced by about an order of magnitude for the same number of users.  It introduces a new mixed-mode network MIMO architecture that is based on a clever partitioning of users according to "geographical bins".  In addition, it proposes an advanced technique for coordination among adjacent base station sites, which improves performance and reduces the number of antennas that need to cooperate coherently.

J. Hoydis, S. ten Brink, and M. Debbah, “Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need?,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 160-171, February 2013.

This paper considers the impact of limiting the number of antennas so that it is not necessarily “much larger” than the number of terminals.  In particular, it determines if the conclusions found for the limiting case of a very large number of antennas hold with a limited number of antennas.  To answer this question, the paper develops a unified performance analysis that accounts for imperfect channel estimation, pilot contamination, antenna correlation, and path loss. Random-matrix-theory based approximations of achievable rates, using the deterministic equivalent framework, are derived for different linear precoders and detectors.

J. Hoydis, K. Hosseini, S. ten Brink, and M. Debbah, “Making Smart Use of Excess Antennas: Massive MIMO, Small Cells, and TDD,” Bell Labs Technical Journal, vol. 18, no. 2, pp. 5-21, September 2013.

This paper elaborates on the benefits of TDD operation, and describes the interplay between small cells and massive MIMO deployment.  A heterogeneous architecture is proposed involving macro cells with very large antenna arrays combined with small cells with few antennas.

H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta, “Cell-Free Massive MIMO Versus Small Cells," IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017.

This paper considers a distributed massive MIMO system where single- antenna base stations coordinate to serve a smaller number of users.

H. Q. Ngo, E. G. Larsson, and T. L. Marzetta, “Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems,” IEEE Transactions on Communications, vol. 61, no. 4, pp. 1436-1449, April 2013.

This paper presents a rigorous analysis of the massive MIMO uplink, for single-cell and multi-cell systems.  Specifically the paper gives closed-form expressions for lower bounds on capacity in such systems.

H. Yang and T. L. Marzetta, “Performance of Conjugate and Zero-Forcing Beamforming in Large-Scale Antenna Systems,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 172–179, February 2013.

This paper gives a capacity bound analysis of the massive MIMO downlink, for single-cell systems.  Rigorous closed-form expressions of performance for zero-forcing processing and conjugate beamforming are given.

H. Yin, D. Gesbert, M. Filippou, and Y. Liu, “A Coordinated Approach to Channel Estimation in Large-Scale Multiple-Antenna Systems,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 264-273, February 2013.

This paper presents a clever approach for handling the pilot contamination problem by enabling a low-rate coordination between cells during the channel estimation phase.  The coordination makes use of second-order statistical information about the user channels.

R. R. Muller, L. Cottatellucci, and M. Vehkapera, “Blind Pilot Decontamination,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 773-786, October 2014.

This paper presents a blind method for non-linear channel estimation that can alleviate the problem of pilot contamination in multi-cell massive MIMO networks.

K. T. Truong and R. W. Heath, “Effects of Channel Aging in Massive MIMO Systems,” Journal of Communications and Networks (JCN), vol. 15, no. 4, pp. 338-351, August 2013.

This paper considers the impact of channel aging on the performance of massive MIMO systems.  Analytical results show how capacity is lost due to the time variation of the channel, and channel prediction is proposed to overcome the negative effects of channel aging.

J. Choi, Z. Chance, D. J. Love, and U. Madhow, “Noncoherent Trellis Coded Quantization: A Practical Limited Feedback Technique for Massive MIMO Systems,” IEEE Transactions on Communications, vol. 61, no. 12, pp. 5016-5029, December 2013.

The paper proposes an efficient method for quantizing channel state information to be fed back from the mobile to the base station. The idea is to reformulate the high-dimensional vector limited-feedback problem as a trellis quantization in the spatial dimension problem that can be solved using Viterbi-like algorithms. Such feedback allows a massive MIMO system to operate in a FDD mode, thereby providing backward compatibility with current-generation systems.

A. Adhikary, J. Nam, J.-Y. Ahn, and G. Caire, “Joint Spatial Division and Multiplexing — The Large-Scale Array Regime,” IEEE Transactions on Information Theory, vol. 59, no. 10, pp. 6441–6463, October 2013.

An approach called “joint spatial division and multiplexing” (JSDM) is proposed for the FDD massive MIMO downlink.  The guiding philosophy is to exploit the structure of the correlation of the channel vectors to permit a large number of base-station antennas while only requiring channel state information with reduced dimensionality at the transmitter. The solution involves a concatenation of a pre-beamformer, which depends only on the second-order channel statistics, followed by a classical multiuser beamformer, which uses instantaneous knowledge of a reduced-dimensionality “effective” channel matrix.  Numerical results are presented that are obtained using asymptotic random matrix theory.

J. Chen and V. Lau, “Two-Tier Precoding for FDD Multi-Cell Massive MIMO Time-Varying Interference Networks,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1230-1238, June 2014.

Similar to JSDM, a two-stage precoder is proposed that is well suited for massive MIMO for multi-cell FDD networks.  The outer precoder is for interference cancelation and is based on the long-term spatial statistics, while the inner precoder is for multiplexing and is adaptive to real-time channel state information.  A compensated subspace-tracking algorithm, which is formulated on the Grassmannian manifold, is proposed for the online computation of the outer precoder.  Control theory is used to characterize the tracking performance of the outer precoder in time-varying channels.

J. Choi, D. Love, and P. Bidigare, “Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training with Memory,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 802-814, October 2014.

Practical open-loop and closed-loop training frameworks are proposed for FDD-based massive MIMO.  Practical MIMO channels are considered, which are correlated in both time and space, and the training algorithms have memory that exploits these correlations.  The open-loop scheme involves the base station transmitting pilots taken from a codebook in a round-robin manner, and the user successively estimating the current channel with the guidance of long-term channel estimates.  The closed-loop scheme involves the user feeding back the index of the best training signal based on channel prediction, and this feedback signal is very compact.

E. Björnson, J. Hoydis, M. Kountouris, and M. Debbah, “Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits,” IEEE Transactions on Information Theory, vol. 60, no. 11, pp. 7112-7139, November 2014.

This paper presents a comprehensive analysis of the impact of hardware impairments on the performance of massive MIMO.  It considers the asymptotic behavior and provides closed-form rate expressions for a finite number of antennas.  These expressions are used to draw various important and far-reaching conclusions.  In particular, they explain the feasibility of building massive MIMO systems from inexpensive hardware components.

C. Studer and E. G. Larsson, "PAR-Aware Large-Scale Multi-User MIMO-OFDM Downlink", IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 303-313, February 2013.

This paper illustrates how the excess degrees-of-freedom in the massive MIMO downlink can be exploited to facilitate hardware-friendly waveform shaping.  In particular, it focuses on the generation of downlink waveforms with low peak-to-average ratio.

J. Hoydis, C. Hoek, T. Wild, and S. ten Brink, “Channel Measurements for Large Antenna Arrays,” in Proc. IEEE International Symposium on Wireless Communication Systems (ISWCS), Paris, France, August 2012.

This paper shows some initial channel measurements for massive MIMO using an outdoor virtual antenna array consisting of up to 112 elements.

C. Shepard, H. Yu, N. Anand, L. E. Li, T. L. Marzetta, R. Yang, and L. Zhong, “Argos: Practical Many-Antenna Base Stations,” in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), August 2012. 

This paper describes an operational massive MIMO testbed with 64 antennas, and particularly gives algorithms for TDD reciprocity calibration, showing the feasibility of TDD.

X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, “Massive MIMO Performance Evaluation Based on Measured Propagation Data," IEEE Transactions on Wireless Communications, vol. 14, no. 7, pp. 3899-3911, July 2015.

This paper presents results from an extensive set of massive MIMO measurements outdoors, with a linear and a cylindrical array, at the 2.6 GHz band. The investigation showed that the measured channels, for both array types, yield performance comparable to that of i.i.d. Rayleigh fading channels.

X. Gao, O. Edfors, F. Tufvesson, and E. G. Larsson, “Massive MIMO in Real Propagation Environments: Do All Antennas Contribute Equally?," IEEE Transactions on Communications, vol. 63, no. 11, pp. 3917-3928, November 2015.

This paper considers measurements from the same arrays, outdoors in the 2.6 GHz band.  The specific focus here is on the potential reduction of the number of RF chains through antenna selection and the associated performance tradeoffs.

P. Harris, S. Malkowsky, J. Vieira, E. Bengtsson, F. Tufvesson, W. Boukley Hasan, L. Liu, M. Beach, S. Armour, and O. Edfors, “Performance Characterization of a Real-Time Massive MIMO System with LOS Mobile Channels,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 6, pp. 1244-1253, June 2017.

This paper details a testbed built from state-of-the-art National Instruments equipment, operating at 3.7 GHz carrier frequency.  The paper describes experiments with the testbed, that demonstrate the feasibility of reciprocity-based (TDD) Massive MIMO in outdoor environments under high mobility.