Tom Marzetta of Bell Labs has been called the "Father of Massive MIMO." His 2010 paper, Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas, has been cited over 1700 times. He and Bell Labs colleague Gerry Foschini have been working on MIMO since the 1990's and made many contributions. For those who want to understand in depth, I've included the abstract of that paper and others below. 

In 2014, Tom told me he thought it would take several more years for practical systems. Masayoshi Son of Softbank was unwilling to wait and launched the first commercial deployment in September, 2016. Softbank is installing 100 systems across 43 cities in Japan. Softbank's early results, from five cities, show a 5X to 10X improvement in the same spectrum. They use 128 antennas. Some of the antennas are used for "beamforming," which is proving crucial for deployments.  Many top engineers expect a 50X improvement from MIMO in the coming years. Remarkably, the increased performance does not require significantly more power. 

Marzetta writes, "Massive MIMO is the most promising technology available to address the ever increasing demand for wireless throughput:

• Orders of magnitude spectral efficiency gains over LTE - large numbers of users communicate simultaneously over entire allotted spectrum through elementary multiplexing signal processing
• Uniformly excellent service throughout the cell - regardless of location relative to base station
• Drastically reduced radiated power
• Simple and scalable design - employs measured channel characteristics rather than assumed channel characteristics
• Naturally green technology - superior energy efficiency

Here are some sources to start your research.

Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas Thomas L. Marzetta 

Abstract
A cellular base station serves a multiplicity of single-antenna terminals over the same time-frequency interval. Time-division duplex operation combined with reverse-link pilots enables the base station to estimate the reciprocal forward- and reverse-link channels. The conjugate-transpose of the channel estimates are used as a linear precoder and combiner respectively on the forward and reverse links. Propagation, unknown to both terminals and base station, comprises fast fading, log-normal shadow fading, and geometric attenuation. In the limit of an infinite number of antennas a complete multi-cellular analysis, which accounts for inter-cellular interference and the overhead and errors associated with channel-state information, yields a number of mathematically exact conclusions and points to a desirable direction towards which cellular wireless could evolve. In particular the effects of uncorrelated noise and fast fading vanish, throughput and the number of terminals are independent of the size of the cells, spectral efficiency is independent of bandwidth, and the required transmitted energy per bit vanishes. The only remaining impairment is inter-cellular interference caused by re-use of the pilot sequences in other cells (pilot contamination) which does not vanish with unlimited number of antennas.
Abstract:

Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].
Published in: IEEE Signal Processing Magazine ( Volume: 30, Issue: 1, Jan. 2013 )

Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems

Hien Quoc Ngo, Erik G. Larsson, and Thomas L. Marzetta

Abstract A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. Lower capacity bounds for maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) detection are derived. A MRC receiver normally performs worse than ZF and MMSE. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option. The tradeoff between the energy efficiency (as measured in bits/J) and spectral efficiency (as measured in bits/channel use/terminal) is quantified. It is shown that the use of moderately large antenna arrays can improve the spectral and energy efficiency with orders of magnitude compared to a single-antenna system. Index Terms Energy efficiency, spectral efficiency, multiuser MIMO, very large MIMO systems I. INTRODUCTION In multiuser

Hien Quoc Ngo, Erik G. Larsson, and Thomas L. Marzetta Abstract A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. Lower capacity bounds for maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) detection are derived. A MRC receiver normally performs worse than ZF and MMSE. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option. The tradeoff between the energy efficiency (as measured in bits/J) and spectral efficiency (as measured in bits/channel use/terminal) is quantified. It is shown that the use of moderately large antenna arrays can improve the spectral and energy efficiency with orders of magnitude compared to a single-antenna system. Index Terms Energy efficiency, spectral efficiency, multiuser MIMO, very large MIMO systems I. INTRODUCTION In multiuser multiple-input multiple-output (MU-MIMO) systems, a base station (BS) equipped with multiple antennas serves a number of users. Such systems have attracted much attention for some time now [2]. Conventionally, the communication between the BS and the users is performed by orthogonalizing the channel so that the BS communicates with each user in separate time-frequency resources. This is not optimal from an information-theoretic point of view, and higher rates can be achieved if the BS communicates with several users in the same time-frequency resource [3], [4]. However, complex techniques to mitigate inter-user interference must then be used, such as maximum-likelihood multiuser detection on the uplink [5], or “dirty-paper coding” on the downlink [6], [7]. Recently, there has been a great deal of interest in MU-MIMO with very large antenna arrays at the BS. Very large arrays can substantially reduce intracell interference with simple signal processing.

 

Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.
Published in: IEEE Communications Magazine ( Volume: 52, Issue: 2, February 2014 )

 

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Wireless Abundance is here: What the new tech means http://bit.ly/Wirelessabundance
Sprint & T-Mobile Charge to be 1st in U.S. to Gig LTE bit.ly/STMOgig  AT&T bit.ly/ATTGIG2016
Kitahara of Softbank “I am crazy about Massive MIMO” http://bit.ly/MMIMOCrazy
20 Gig mmWave, Massive MIMO & Gig LTE at the Huawei MBBF http://bit.ly/Huawei20
LTE gets to the gigabit explained for non-engineers http://bit.ly/GigLteexplained
Massive MIMO explained. http://bit.ly/WHMassiveMIMO
2017's Big Gigabit story: Qualcomm 835 is ready http://bit.ly/BigGigLTE
Doubling speed with 4x4 MIMO & 256 QAM at T-Mobile http://bit.ly/2k1gEOQ
Netgear Nighthawk M1, Telstra do "gigabit class" LTE http://bit.ly/2k1s5Gq
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dave right5G? 4G? Whatever the name, wireless is going to a gigabit, soon.  I've reported broadband since 1999 and now is the time for gigabit wireless. Catch a mistake or have news? Email me please. Dave Burstein

 

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