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Massive MIMO makes it possible to increase data throughput and
coverage in real-world networks. Such active antenna arrays become
indispensable for 5G base stations. Here is a concise overview of key
aspects of massive MIMO technology.
www.linkedin.com/in/pavithra-nagaraj
By – Pavithra Nagaraj, 5G Researcher

Before we step in to Massive MIMO, it is
important to understand the basic processes.
In wireless communications, there are four types of antenna systems:
1. Single-input/Single-output (SISO)
2. Single-input/ Multiple-output (SIMO)
3. Multiple-input/Single-output (MISO)
4. Multiple-input/Multiple-output (MIMO)
With one antenna on either side, SISO provides no diversity protection
against fading.
Compared to that configuration, the use of multiple antennas on the
transmitter side, the receiver side, or both, can improve reliability, capacity,
or both:

– SISO: As stated earlier, this configuration
creates no diversity.
– SIMO: This configuration creates receiver
diversity, uses smart antennas to implement
beamforming, and provides an improved SINR
(signal-to-interference-plus-noise ratio).
– MISO: This configuration creates transmitter
diversity, uses smart antennas to implement
beamforming, and improves SINR.
– MIMO: This configuration creates both
transmitter & receiver diversity, uses smart
antennas to implement beamforming on both
sides, improves SINR, and provides greater
spectral efficiency.

More on MIMO
• As stated in the earlier slide, Multiple input multiple output (MIMO)
is a transmission technology that comprising multiple antennas for
communication at sources and their destination to improve the
diversity gain.
• Allows sending and receiving of more than one data signal on the
same channel at the same time by using more than one antenna,
thus improving the data rates between the transmitter and the
receiver.
• It also takes advantage of uncorrelated propagation paths for higher
efficiency and high throughput and/or to allow simultaneous access
for different users.

What is Single-User MIMO?
• This technique is used to increase
the data rate to a specific user, and
it is currently being used in LTE as
well as 802.11n and 802.11ac.
• In single-user MIMO, the transmitter
multiplexes the data for one user
across two or more independent
radios and antennas. Each receive
antenna will see a combination of
the signals from all of the transmit
antennas.

• Part of the transmitted data will be a known sequence of pilot signals
or a preamble. The receiver will use the known data to calculate the
channel matrix, H, and once that matrix is known, the receiver can
then use it to decode the unknown data transmission.
• The transmitter does not need to have any knowledge about the
channel. All of the required extra computation is done in the
receiver—and putting this heavy computational burden on battery-
powered user equipment (UE) is not ideal.
• In the example case shown in figure, the receiver de-multiplexes the
two data streams based on knowledge of the channel [H].

• We can use matrix math to express the direct and cross interactions
within this system:
From this,
Single-user MIMO requires a multipath environment to allow the
receiver to correctly generate the H matrix, which is needed to decode
the received signals.

What is Multi-User MIMO?
• Multi-user MIMO has several
differences from single-user MIMO.
• It uses multiple antennas on a single
transmitter and there can be several
independent receivers, each with
one antenna.
• Another difference: the transmitter
pre-codes the data, shown as the W
matrix in the figure.

We can use matrix math to express the direct and cross interactions within
this system on the transmitter and receiver sides:
• As shown in the matrix, the signal transmitted on each antenna, x0 and x1,
is a combination of the symbols for each user, s0 and s1. On the receiver
side, the basic process proceeds as follows:
– For user 0, the components of s0 from all antennas arrive in phase, and
thus add. The components of s1 arrive out of phase and thus cancel, leaving
only s0 at the first receiver.
– For user 1, the s0 signals cancel and the s1 signals add, leaving only s1 at
the second user’s input.

The difficult part of the process lies in how the transmitter learns the
channel state that is needed to generate the W matrix. Several
approaches are possible; however, a detailed exploration of these is
beyond the scope of this note.
In single-user MIMO, the knowledge of the channel is in the receiver; in
multi-user MIMO the knowledge of the channel is in the transmitter.
Because all of the power-consuming calculations are performed in the
transmitter, this approach is more attractive for any system in which
the receivers are battery-powered.
“Massive MIMO is just multi-user MIMO with a number of base station
antennas that far exceeds the number of user terminals.”

Now it’s time to jump deep into Massive MIMO…
• As per the understanding till now, Massive MIMO is just multi-user
MIMO with a number of BS antennas (M) that far exceeds the number
of user terminals (K).
• In massive MIMO systems, a very high number of antenna elements is
used at the transceiver, which allows two major concepts to be
dynamically combined: beamforming and spatial multiplexing, both
brought about by the ability of the many antenna elements to focus
their energy into smaller regions of space. If an antenna system can
do this, we refer to that antenna system as massive MIMO.
• Massive MIMO is mainly applied at base stations.
• 5G user devices may implement basic beamforming schemes.

Origin of the term ‘Massive MIMO’
• ‘A dear child has many names’ is a famous saying and it certainly applies to
Massive MIMO.
• The Massive MIMO concept originates from the seminar paper “Non-
cooperative cellular wireless with unlimited number of Base station
antennas” published by Thomas Marzetta in 2010. The paper talks about
“Multi-user system with very large antenna arrays”. Then Marzetta
published several papers using the LSAS (large-scale antenna systems)
terminology before switching to call it as the ‘Massive MIMO’.
• Over years, many papers have also been published and called it ‘Very large
multiuser MIMO’ and ‘Large scale MIMO’.
• In recent times Massive MIMO is used by almost everyone in the industry,
because it is indeed a catchy name compared to earlier ones.

Definitions of Massive MIMO:
1. Massive MIMO is a useful and scalable version of Multiuser MIMO.
There are three fundamental distinctions between Massive MIMO and
conventional Multiuser MIMO.
• First, only the base station learns G (The Channel Matrix).
• Second, M (No. of antennas) is typically larger than K (No. of users).
• Third, Simple linear signal processing is used both on the uplink and
the downlink.
All these feature render Massive MIMO scalable with respect to the
base station antennas (M).

2. Massive MIMO is a multi-user MIMO system with M antennas and K
users per Base station. The system is characterized by M>>K and
operates in TDD mode using linear uplink and downlink processing.
A more precise definition can be written as such:
“Massive MIMO is a multi-user MIMO system that serves multiple
users through spatial multiplexing over a channel with favourable
propagation (when users being mutually orthogonal) in time-division
duplex, and relies on channel reciprocity & uplink pilots to obtain
channel state information.”

Why use Massive MIMO?
There are many advantages to using massive MIMO with beamforming.
• The most important one is the improvement in energy efficiency that results
from increasing the antenna gain by bundling the transmitted energy. Doing
this also increases the range and reduces inter-cell interference.
• At higher frequencies (millimetre waves), there is an additional challenge of
higher path loss, but the advantage of smaller antenna size.
• At higher frequencies, the large number of antenna elements can be used to
generate a very narrow beam with large gain, and at lower frequencies, the
large number of antenna elements can be used to generate multiple spatial
streams.

What are the biggest challenges with Massive MIMO?
Although it has many advantages, massive
MIMO has many challenges to consider.
1. Data Bottleneck
There is the potential to create a data
bottleneck due to the large amount of data
being sent and received by the massive MIMO
antenna systems as part of a centralized RAN
system, requiring adequate fiber bandwidth.

2. Calibration
Given the large number of antenna elements, beamforming antennas
that are not calibrated properly will suffer from unwanted emissions in
unwanted directions, e.g. beam squint, which is basically jitter of the
beam boresight.

3. Mutual coupling
Mutual Coupling between antenna elements results in energy loss and
thus a reduction in the maximum range.

4. Irregular Arrays
In going from theory to practice, some antenna arrays will need to be
designed in non-geometric shapes that may result in dissipating energy
in undesired directions.

5. Complexity
Massive MIMO antenna systems represent
a new level of complexity from a design,
manufacturing, calibration and deployment
perspective. And with this new level of
complexity also comes the need for new
design and test approaches.

Different types of beamforming used for Massive
MIMO systems?
Achieving a certain directivity or beamforming requires an antenna
array where the RF signal at each antenna element is amplitude and
phase weighted.
There are three possible ways to apply amplitude and phase shifts.
1. Analog Beamforming
2. Digital Beamforming
3. Hybrid Beamforming

Analog Beamforming
The traditional approach, used for example in radar applications, applies
an antenna array combined with phase shifters and power amplifiers to
steer the beam into the desired direction, reducing the sidelobes to a
minimum. The small amount of hardware makes this a cost-effective
method for building a beamforming array of a certain complexity. Typically,
an analog beamforming array is connected to one RF chain generating only
one beam at a time, and the range of the phase shifters used limits the
applicable frequency range.

Digital Beamforming
The more advanced architecture performs phase and amplitude weighting
in the digital domain. Each antenna has its own transceiver and data
converters, allowing it to handle multiple data streams and generate
multiple beams simultaneously from one array. In addition to beam
steering, null steering for interference reduction is also feasible. Digital
beamforming requires A/D converters, making it challenging at higher
frequencies.

Hybrid Beamforming
Hybrid beamforming balances the advantages & disadvantages of analog
and digital beamforming.
Targeting higher frequency ranges, such designs combine multiple
antenna array elements into subarray modules that are connected to a
digital pre-processing stage.
System designers use hybrid beamforming to balance flexibility and cost
trade-offs while still meeting the required performance parameters such
as number of simultaneous beams and frequency range.

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