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Massive MIMO for next generation wireless systems

TL;DR: 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.
Abstract: 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.

Summary (2 min read)

Introduction

  • Multi-user Multiple-Input Multiple-Output (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.
  • 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.
  • Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency.
  • The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard.
  • This paper presents an overview of the massive MIMO concept and of contemporary research on the topic.

2 Going Large: Massive MIMO

  • The authors follow up on their earlier exposition [1], with a focus on the developments in the last three years: most particularly, energy efficiency, exploitation of excess degrees of freedom, TDD calibration, techniques to combat pilot contamination, and entirely new channel measurements.
  • With massive MIMO, the authors think of systems that use antenna arrays with a few hundred antennas, simultaneously serving many tens of terminals in the same time-frequency resource.
  • On the uplink, this is easy to accomplish by having the terminals send pilots, based on which the base station estimates the channel responses to each of the terminals.
  • Second, the number of channel responses that each terminal must estimate is also proportional to the number of base station antennas.

3 The Potential of Massive MIMO

  • Massive MIMO technology relies on phase-coherent but computationally very simple processing of signals from all the antennas at the base station.
  • Downlink data is transmitted via maximum-ratio transmission (MRT) beamforming combined with power control, where the 5% of the terminals having the worst channels are excluded from service.
  • As a bonus, the total emitted power can be dramatically cut and therefore the base station will generate substantially less electromagnetic interference.
  • Massive MIMO relies on the law of large numbers and beamforming in order to avoid fading dips, so that fading no longer limits latency.
  • Intentional jamming of civilian wireless systems is a growing concern and a serious cybersecurity threat that seems to be little known to the public.

4.1 Channel Reciprocity

  • The hardware chains in the base station and terminal transceivers may not be reciprocal between the uplink and the downlink.
  • Calibration of the hardware chains does not seem to constitute a serious problem and there are calibrationbased solutions that have already been tested to some extent in practice [3,12].
  • Specifically, [3] treats reciprocity calibration for a 64-antenna system in some detail and claims a successful experimental implementation.
  • Note that calibration of the terminal uplink and downlink chains is not required in order to obtain the full beamforming gains of massive MIMO: if the base station equipment is properly calibrated then the array will indeed transmit a coherent beam to the terminal.

4.2 Pilot Contamination

  • Ideally every terminal in a Massive MIMO system is assigned an orthogonal uplink pilot sequence.
  • The effect of re-using pilots from one cell to another, and the associated negative consequences, is termed “pilot contamination”.
  • Similar interference is associated with uplink transmissions of data.
  • In [13] 11 it was argued that pilot contamination constitutes an ultimate limit on performance, when the number of antennas is increased without bound, at least with receivers that rely on pilot-based channel estimation.

4.3 Radio Propagation and Orthogonality of Channel Responses

  • Massive MIMO (and especially MRC/MRT processing) relies to a large extent on a property of the radio environment called favorable propagation.
  • Fig. 5 shows pictures of the two massive MIMO arrays used for the measurements reported in this paper.
  • More specifically, the figure shows the cumulative density function (CDF) of the difference between the smallest and the largest singular value for the different measured frequency points in the different cases.
  • The compact circular array has inferior performance compared to the linear array owing to its smaller aperture – it cannot resolve the scatterers as well as the physically large array – and its directive antenna elements sometimes pointing in the wrong direction.
  • Those conclusions are also in line with the observations in [5] where another outdoor measurement campaign is described and analyzed.

5 Massive MIMO: a Goldmine of Research Problems

  • While massive MIMO renders many traditional problems in communication theory less relevant, it uncovers entirely new problems that need research: Fast and distributed, coherent signal processing.
  • With low-cost phase locked-loops or even free-running oscillators at each antenna, phase noise may become a limiting factor.
  • Massive MIMO offers the potential to reduce the radiated power a thousand times, and at the same time drastically scale up data rates.
  • It is also important to develop more sophisticated analytical channel models.
  • In particular, the testbed shows that TDD operation relying on channel reciprocity is possible.

6 Conclusions and Outlook

  • The technology offers huge advantages in terms of energy efficiency, spectral efficiency, robustness and reliability.
  • It allows for the use of low-cost hardware both at the base station as well as at the mobile unit side.
  • There are still challenges ahead to realize the full potential of the technology, e.g., when it comes to computational complexity, realization of distributed processing algorithms, and synchronization of the antenna units.
  • This gives researchers both in academia and industry a goldmine of entirely new research problems to tackle.

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Massive MIMO for Next Generation Wireless
Systems
Erik G Larsson, Ove Edfors, Fredrik Tufvesson and Thomas L. Marzetta
Linköping University Post Print
N.B.: When citing this work, cite the original article.
Erik G Larsson, Ove Edfors, Fredrik Tufvesson and Thomas L. Marzetta, Massive MIMO for
Next Generation Wireless Systems, 2014, IEEE Communications Magazine, (52), 2, 186-195.
http://dx.doi.org/10.1109/MCOM.2014.6736761
©2014 IEEE. Personal use of this material is permitted. However, permission to
reprint/republish this material for advertising or promotional purposes or for creating new
collective works for resale or redistribution to servers or lists, or to reuse any copyrighted
component of this work in other works must be obtained from the IEEE.
http://ieeexplore.ieee.org/
Postprint available at: Linköping University Electronic Press
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105585

1
MASSIVE MIMO FOR NEXT GENERATION WIRELESS SYSTEMS
Erik G. Larsson, ISY, Linköping University, Sweden
1
Ove Edfors, Lund University, Sweden
Fredrik Tufvesson, Lund University, Sweden
Thomas L. Marzetta, Bell Labs, Alcatel-Lucent, USA
Abstract
Multi-user Multiple-Input Multiple-Output (MIMO) offers big advantages over conven-
tional 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 ter-
minal 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 cur-
rent 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 the extensive use of inexpensive low-power com-
ponents, reduced latency, simplification of the media access control (MAC) layer, and ro-
bustness to intentional jamming. The anticipated throughput depend on the propagation
environment providing asymptotically orthogonal channels to the terminals, but so far ex-
periments have not disclosed any limitations in this regard. While massive MIMO renders
many traditional research problems irrelevant, it uncovers entirely new problems that ur-
gently 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 paper presents an overview of the massive MIMO
concept and of contemporary research on the topic.
1 Background: Multi-User MIMO Maturing
MIMO, Multiple-Input Multiple Output, technology relies on multiple antennas to simultane-
ously transmit multiple streams of data in wireless communication systems. When MIMO is
used to communicate with several terminals at the same time, we speak of multiuser MIMO.
Here, we just say MU-MIMO for short.
1
Contact email: egl@isy.liu.se

2
MU-MIMO in cellular systems brings improvements on four fronts:
increased data rate, because the more antennas, the more independent data streams can
be sent out and the more terminals can be served simultaneously;
enhanced reliability, because the more antennas the more distinct paths that the radio
signal can propagate over;
improved energy efficiency, because the base station can focus its emitted energy into the
spatial directions where it knows that the terminals are located; and
reduced interference because the base station can purposely avoid transmitting into direc-
tions where spreading interference would be harmful.
All improvements cannot be achieved simultaneously, and there are requirements on the prop-
agation conditions, but the four above bullets are the general benefits. MU-MIMO technology
for wireless communications in its conventional form is maturing, and incorporated into re-
cent and evolving wireless broadband standards like 4G LTE and LTE-Advanced (LTE-A). The
more antennas the base station (or terminals) are equipped with, the better performance in all
the above four respects—at least for operation in time-division duplexing (TDD) mode. How-
ever, the number of antennas used today is modest. The most modern standard, LTE-Advanced,
allows for up to 8 antenna ports at the base station and equipment being built today has much
fewer antennas than that.
2 Going Large: Massive MIMO
Massive MIMO is an emerging technology, that scales up MIMO by possibly orders of mag-
nitude compared to current state-of-the-art. In this paper, we follow up on our earlier expo-
sition [1], with a focus on the developments in the last three years: most particularly, energy
efficiency, exploitation of excess degrees of freedom, TDD calibration, techniques to combat
pilot contamination, and entirely new channel measurements.
With massive MIMO, we think of systems that use antenna arrays with a few hundred antennas,
simultaneously serving many tens of terminals in the same time-frequency resource. The basic
premise behind massive MIMO is to reap all the benefits of conventional MIMO, but on a much
greater scale. Overall, massive MIMO is an enabler for the development of future broadband
(fixed and mobile) networks which will be energy-efficient, secure, and robust, and will use the
spectrum efficiently. As such, it is an enabler for the future digital society infrastructure that will
connect the Internet of people, Internet of things, with clouds and other network infrastructure.
Many different configurations and deployment scenarios for the actual antenna arrays used by

3
a massive MIMO system can be envisioned, see Fig. 1. Each antenna unit would be small, and
active, preferably fed via an optical or electric digital bus.
Massive MIMO relies on spatial multiplexing that in turn relies on the base station having good
enough channel knowledge, both on the uplink and the downlink. On the uplink, this is easy
to accomplish by having the terminals send pilots, based on which the base station estimates
the channel responses to each of the terminals. The downlink is more difficult. In conventional
MIMO systems, like the LTE standard, the base station sends out pilot waveforms based on
which the terminals estimate the channel responses, quantize the so-obtained estimates and
feed them back to the base station. This will not be feasible in massive MIMO systems, at least
not when operating in a high-mobility environment, for two reasons. First, optimal downlink
pilots should be mutually orthogonal between the antennas. This means that the amount of time-
frequency resources needed for downlink pilots scales as the number of antennas, so a massive
MIMO system would require up to a hundred times more such resources than a conventional
system. Second, the number of channel responses that each terminal must estimate is also
proportional to the number of base station antennas. Hence, the uplink resources needed to
inform the base station about the channel responses would be up to a hundred times larger
than in conventional systems. Generally, the solution is to operate in TDD mode, and rely
on reciprocity between the uplink and downlink channels—although FDD operation may be
possible in certain cases [2].
While the concepts of massive MIMO have been mostly theoretical so far, and in particular
stimulated much research in random matrix theory and related mathematics, basic testbeds are
becoming available [3] and initial channel measurements have been performed [4, 5].
3 The Potential of Massive MIMO
Massive MIMO technology relies on phase-coherent but computationally very simple process-
ing of signals from all the antennas at the base station. Some specific benefits of a massive
MU-MIMO system are:
Massive MIMO can increase the capacity 10 times or more and simultaneously, improve
the radiated energy-efficiency in the order of 100 times.
The capacity increase results from the aggressive spatial multiplexing used in massive
MIMO. The fundamental principle that makes the dramatic increase in energy efficiency
possible is that with large number of antennas, energy can be focused with extreme sharp-
ness into small regions in space, see Fig. 2. The underlying physics is coherent superpo-
sition of wavefronts. By appropriately shaping the signals sent out by the antennas, the
base station can make sure that all wave fronts collectively emitted by all antennas add
up constructively at the locations of the intended terminals, but destructively (randomly)

4
Figure 1: Some possible antenna configurations and deployment scenarios for a massive MIMO base
station.
almost everywhere else. Interference between terminals can be suppressed even further
by using, e.g., zero-forcing (ZF). This, however, may come at the cost of more transmitted
power, as illustrated in Fig. 2.
More quantitatively, Fig. 3 (from [6]) depicts the fundamental tradeoff between the energy
efficiency in terms of the total number of bits (sum-rate) transmitted per Joule per terminal
receiving service of energy spent, and spectral efficiency in terms of total number of bits
(sum-rate) transmitted per unit of radio spectrum consumed. The figure illustrates the
relation for the uplink, from the terminals to the base station (the downlink performance
is similar). The figure shows the tradeoff for three cases:
a reference system with one single antenna serving a single terminal (purple),
a system with 100 antennas serving a single terminal using conventional beamform-
ing (green)
a massive MIMO system with 100 antennas simultaneously serving multiple (about
40 here) terminals (red, using maximum-ratio combining; and blue, using zero-
forcing).
The attractiveness of maximum-ratio combining (MRC) compared with ZF is not only its
computational simplicity—multiplication of the received signals by the conjugate channel
responses, but also that it can be performed in a distributed fashion, independently at each
antenna unit. While ZF also works fairly well for a conventional or moderately-sized

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Cites background or methods from "Massive MIMO for next generation wi..."

  • ...Emitted wavefronts add constructively at the intended location and reduces their strength everywhere else [92]....

    [...]

  • ...Moreover, inexpensive and low-power components can be used to build massive MIMO [92]....

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Posted Content
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
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  • ...exploiting new spectrum resources [65], [66], designing novel transceiver architectures [67]–[69] and network paradigms [70], [71] to improve the spectrum efficiency,...

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References
More filters
Journal ArticleDOI
Thomas L. Marzetta1
TL;DR: A cellular base station serves a multiplicity of single-antenna terminals over the same time-frequency interval and a complete multi-cellular analysis yields a number of mathematically exact conclusions and points to a desirable direction towards which cellular wireless could evolve.
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.

6,248 citations


"Massive MIMO for next generation wi..." refers background in this paper

  • ...In [13], for a typical operating scenario, the maximum number of orthogonal pilot sequences in a one millisecond coherence interval is estimated to be about 200....

    [...]

  • ...Pilot contamination as a basic phenomenon is not really specific to massive MIMO, but its effect on massive MIMO appears to be much more profound than in classical MIMO [13,14]....

    [...]

  • ...This directed interference grows with the number of service-antennas at the same rate as the desired signal [13]....

    [...]

Journal ArticleDOI
TL;DR: 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.
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].

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  • ...In this paper, we follow up on our earlier exposition [1], with a focus on the developments in the last three years: most particularly, energy efficiency, exploitation of excess degrees of freedom, TDD calibration, techniques to combat pilot contamination, and entirely new channel measurements....

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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. An 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 for a channel model that includes small-scale fading but not large-scale fading. 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.

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Journal ArticleDOI
TL;DR: Very large MIMO as mentioned in this paper is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation.
Abstract: This paper surveys recent advances in the area of very large MIMO systems. With very large MIMO, we think of systems that use antenna arrays with an order of magnitude more elements than in systems being built today, say a hundred antennas or more. Very large MIMO entails an unprecedented number of antennas simultaneously serving a much smaller number of terminals. The disparity in number emerges as a desirable operating condition and a practical one as well. The number of terminals that can be simultaneously served is limited, not by the number of antennas, but rather by our inability to acquire channel-state information for an unlimited number of terminals. Larger numbers of terminals can always be accommodated by combining very large MIMO technology with conventional time- and frequency-division multiplexing via OFDM. Very large MIMO arrays is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation. The ultimate vision of very large MIMO systems is that the antenna array would consist of small active antenna units, plugged into an (optical) fieldbus.

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TL;DR: How many antennas per UT are needed to achieve η% of the ultimate performance limit with infinitely many antennas and how many more antennas are needed with MF and BF to achieve the performance of minimum mean-square error (MMSE) detection and regularized zero-forcing (RZF), respectively are derived.
Abstract: We consider the uplink (UL) and downlink (DL) of non-cooperative multi-cellular time-division duplexing (TDD) systems, assuming that the number N of antennas per base station (BS) and the number K of user terminals (UTs) per cell are large. Our system model accounts for channel estimation, pilot contamination, and an arbitrary path loss and antenna correlation for each link. We derive approximations of achievable rates with several linear precoders and detectors which are proven to be asymptotically tight, but accurate for realistic system dimensions, as shown by simulations. It is known from previous work assuming uncorrelated channels, that as N→∞ while K is fixed, the system performance is limited by pilot contamination, the simplest precoders/detectors, i.e., eigenbeamforming (BF) and matched filter (MF), are optimal, and the transmit power can be made arbitrarily small. We analyze to which extent these conclusions hold in the more realistic setting where N is not extremely large compared to K. In particular, we derive how many antennas per UT are needed to achieve η% of the ultimate performance limit with infinitely many antennas and how many more antennas are needed with MF and BF to achieve the performance of minimum mean-square error (MMSE) detection and regularized zero-forcing (RZF), respectively.

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"Massive MIMO for next generation wi..." refers background in this paper

  • ...Pilot contamination as a basic phenomenon is not really specific to massive MIMO, but its effect on massive MIMO appears to be much more profound than in classical MIMO [13,14]....

    [...]

Frequently Asked Questions (12)
Q1. What was used for the synthetic linear array measurements?

When using the cylindrical array, the RUSK Lund channel sounder was employed, while a network analyzer was used for the synthetic linear array measurements. 

In a massive MIMO system, when using MRC and when operating in the “green” regime, that is, scaling down the power as much as possible without seriously affecting the overall spectral efficiency, multiuser interference and effects from hardware imperfections mostly drown in the thermal noise. 

One-quarter of the time is spent on transmission of uplink pilots for TDD channel estimation, and it is assumed that the channel is substantially constant over intervals of 164 ms in order to estimate the channel gains with sufficient accuracy. 

When operating in the 1 bit/dimension/terminal regime, there is also some evidence that intersymbol interference can be treated as additional thermal noise [7], hence offering a way of disposing with OFDM as a means of combatting intersymbol interference. 

The fundamental principle that makes the dramatic increase in energy efficiency possible is that with large number of antennas, energy can be focused with extreme sharpness into small regions in space, see Fig. 

There are still challenges ahead to realize the full potential of the technology, e.g., when it comes to computational complexity, realization of distributed processing algorithms, and synchronization of the antenna units. 

In [13], for a typical operating scenario, the maximum number of orthogonal pilot sequences in a one millisecond coherence interval is estimated to be about 200. 

Numerical averaging over random terminal locations and over the shadow fading shows that 95% of the terminals will receive a throughput of 21.2 Mb/s/terminal. 

The reason that the overall spectral efficiency still can be 10 times higher than in conventional MIMO is that many tens of terminals are served simultaneously, in the same time-frequency resource. 

This means that the amount of timefrequency resources needed for downlink pilots scales as the number of antennas, so a massive MIMO system would require up to a hundred times more such resources than a conventional system. 

the array in this example will offer the 1000 terminals a total downlink throughput of 20 Gb/s, resulting in a sum-spectral efficiency of 1000 bits/s/Hz. 

It may be possible entirely to forgo reciprocity calibration within the array; for example if the maximum phase difference between the up-link chain and the down-link chain were less than 60 degrees then coherent beam forming would still occur (at least with MRT beamforming) albeit with a possible 3 dB reduction in gain.