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
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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
![Figure 3: Half the power—twice the force (from [6]): Improving uplink spectral efficiency 10 times and simultaneously increasing the radiated-power efficiency 100 times with massive MIMO technology, using extremely simple signal processing—taking into account the energy and bandwidth costs of obtaining channel state information.](/figures/figure-3-half-the-power-twice-the-force-from-6-improving-32pblthm.webp)
