# Range Resolution

```Range Resolution
The target resolution of a radar is its ability to distinguish between targets that are very close
in either range or bearing. Weapons-control radar, which requires great precision, should be
able to distinguish between targets that are only yards apart. Search radar is usually less
precise and only distinguishes between targets that are hundreds of yards or even miles apart.
Resolution is usually divided into two categories; range resolution and bearing resolution.
Range resolution is the ability of a radar system to distinguish between two or more targets on
the same bearing but at different ranges. The degree of range resolution depends on the width
of the transmitted pulse, the types and sizes of targets, and the efficiency of the receiver and
indicator. Pulse width is the primary factor in range resolution. A well-designed radar system,
with all other factors at maximum efficiency, should be able to distinguish targets separated
by one-half the pulse width time τ. Therefore, the theoretical range resolution cell of a radar
system can be calculated from the following equation:
c0 · τ
Sr ≥
(1)
2
Figure 2: Animation: One target includes two aims
The following figures show the range resolution for a pulse with of one microsecond. If the
spacing between two aircrafts is to small, then the radar “see” only one target as shown in
Figure 2.
And now the other example when the spacing is large enough:
Figure 3: Animation: two aims and two targets
Figure 4: Range resolution as a function of transmitters bandwidth
In a pulse compression system, the range-resolution of the radar is given by the length of the
pulse at the output-jack of the pulse compressing stage. The ability to compress the pulse
depends on the bandwidth of the transmitted pulse (BWtx) not by its pulse width. As a matter
of course the receiver needs at least the same bandwidth to process the full spectrum of the
echo signals.
c0
Sr ≥
(2)
2 · BWtx
This allows very high resolution (and a small radar range resolution cell) to be obtained with
long pulses, thus with a higher average power. Figure 4 shows the variation of slant range
resolution with bandwidth. An 1.5 m resolution will be achieved with a -3 dB bandwidth of
100 MHz theoretically.
The size and ability of a target to reflect radar energy can be summarized into a single term, σ,
known as the radar cross-section, which has units of m². This unit shows, that the radar cross
section is an area. If absolutely all of the incident radar energy on the target were reflected
equally in all directions, then the radar cross section would be equal to the target's crosssectional area as seen by the transmitter. In practice, some energy is absorbed and the
reflected energy is not distributed equally in all directions. Therefore, the radar cross-section
is quite difficult to estimate and is normally determined by measurement.
The target radar cross sectional area depends of:
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the airplane’s physical geometry and exterior features,
the direction of the illuminating radar,
the used material types.
The use of stealth technology to reduce radar cross section increases the survivability and
decreases the target detection of military aircraft. But the stealth technology depends of the
used radar transmitters frequency and has none effect against VHF- radars like P–12 or P-18,
both used by serbian air defense units during the Kosovo war.
Calculation of the radar cross section
Radar cross section (RCS) is the measure of a target's ability to reflect radar signals in the
direction of the radar receiver, i.e. it is a measure of the ratio of backscatter density in the
direction of the radar (from the target) to the power density that is intercepted by the target.
Since the power is distributed on the shape of a sphere, a small part of this ((4·π·r2)) can be
Radar cross section σ is as defined as:
4·π·r2·Sr
σ
=
St
σ: measure of the target's ability to reflect radar signals in direction of the radar
(1)
St: power density that is intercepted by the target, in [W/m²]
mit
Sr: scattered power density in the range r, in [W/m²]
The RCS of a target can be viewed as a comparison of the strength of the reflected signal
from a target to the reflected signal from a perfectly smooth sphere of cross sectional area of
1 m².
The following backscattering formulas from shapes occurs in an optical independent of
frequency region.
σmax = π ·R2 (2)
reflected signal from a spherical shape
2·π·r·h2
σmax =
(3)
λ
reflected signal from a cylinder
4·π·b2·h2
σmax =
reflected signal from a flat plate
reflected signal from a tilted plate
(4)
λ
2
...Real as the previous example. Unusual feature: the
reflected energy is reflected in another direction.
energy. Therefore there are bistatic radars at which
the transmitter and the receivers are separated from
each other spatially.
Phased Array Antenna
A phased array antenna is composed of lots of radiating elements each with a phase shifter.
Beams are formed by shifting the phase of the signal emitted from each radiating element, to
provide constructive/destructive interference so as to steer the beams in the desired direction.
The main beam always points in the direction of the increasing phase shift. Well, if the signal
to be radiated is delivered through an electronic phase shifter giving a continuous phase shift
then the beam direction will be electronically adjustable. However, this cannot be extended
unlimitedly. The highest value, which can be achieved for the Field of View (FOV) of a
olanar phased array antenna is 120° (60° left and 60° right). With the sine theorem the
necessary phase moving can be calculated.
The following figure graphically shows the matrix of radiating elements. Arbitrary antenna
constructions can be used as a spotlight in an antenna field. For a phased array antenna is
decisive that the single radiating elements are steered for with a regular phase moving and the
main direction of the beam therefore is changed. E.g. the antenna of the RRP 117 consists of
1584 radiating elements arranged in an analogue beamforming architecture. More
sophsticated radar sets use the benefits of a Digital Beamforming architecture.
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high gain width los side lobes
Ability to permit the beam to jump from one
target to the next in a few microseconds
Ability to provide an agile beam under
computer control
arbitrarily modes of surveillance and tracking
free eligible Dwell Time
multifunction operation by emitting several
beams simultaneously
Fault of single components reduces the
capability and beam sharpness, but the
system remains operational
Possible Arrangements
Linear Arrays
Figure 3: linear array of a phased-array antenna
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the coverage is limited to a 120
degree sector in azimuth and
elevation
deformation of the beam while
the deflection
low frequency agility
very complex structure
(processor, phase shifters)
still high costs
These antennae consist of lines whose elements are fed about a common phase shifter. A
number of vertically about each other mounted linear arrays form a flat antenna.
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Disadvantage: Ray deflection only in a single plane possible
Examples given:
o PAR-80 (horizontal beam-deflection) and
o RRP-117 (vertical beam-deflection)
o Large Vertical Aperture (LVA), an antenna with fixed beam pattern.
This kind of the phased-array antenna is commonly used, if the beam-deflection is required in
a single plane only because a turn of the complete antenna is anyway carried out ( RRP-117).
Planar Arrays
Figure 4: planar array of a phased-array antenna
These antenna arrays completely consist of singles radiating elements and each of it gets an
own phase shifter. The elements are ordered in a matrix array. The planar arrangement of all
elements forms the complete phased-array antenna.
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Advantages: Beam steering in two planes or even the digital beamforming is possible.
Disadvantage: complicated arrangement and more electronically controlled phase shifter
needed
Examples given: AN-FPS-85 and Thomson Master-A
RAR transmits a narrow angle beam of pulse radio wave in the range direction at right
angles to the flight direction (called the azimuth direction) and receives the backscattering
from the targets which will be transformed to a radar image from the received signals, as
shown in Figure 4.2.1.
Usually the reflected pulse will be arranged in the order of return time from the targets, which
corresponds to the range direction scanning.
The resolution in the range direction depends on the pulse width, as shown in Figure 4.2.2.
However if the pulse width is made small, in order to increase the resolution, the S/N ratio of
the return pulse will decrease because the transmitted power also becomes low. Therefore, the
transmitted pulse is modulated to chirp with a high power but wide band, which is received
through a matched filter, with reverse function of transmission, to make the pulse width
very narrow and high power as shown in Figure 4.2.3. This is called pulse compression or
de-chirping. By making the pulse compression, with an increase of frequency f in
transmission, the amplitude becomes
times bigger, and the pulse width becomes 1/TDf
narrower. This method is sometime called range compression.
The resolution in the azimuth direction is identical to the multiplication of beam width and the
distance to a target. As the resolution of azimuth direction increases with shorter wave length
and bigger antenna size, a shorter wavelength and a bigger antenna is used for higher azimuth
resolution, as shown in Figure 4.2.4.
However as it is difficult to attach such a large antenna, requiring for example a 1 km
diameter antenna in order to obtain 25 meters resolution with L band ( =25 cm) and 100 km
distance from a target, a real aperture radar therefore has a technical limitation for improving
the azimuth resolution.
the antenna's size or aperture to increase the azimuth resolution though the same pulse
compression technique as adopted for range direction. Synthetic aperture processing is a
complicated data processing of received signals and phases from moving targets with a small
antenna, the effect of which is to should be theoretically convert to the effect of a large
antenna, that is a synthetic aperture length, as shown as Figure 4.3.1.
The synthetic aperture length is the beam width by range which a real aperture radar of the
same length, can project in the azimuth direction.
The resolution in the azimuth direction is given by half of real aperture radar as shown as
follows.
Real beam width : = /D
Real resolution: L= R=Ls (synthetic aperture length)
Synthetic beam width : s = / 2Ls= D / 2R
Synthetic resolution : Ls = sR = D / 2
where :wavelength D: aperture of radar R: slant range
This is the reason why SAR has a high azimuth resolution with a small size of antenna
regardless of the slant range, or very high altitude of a satellite.
Figure 4.3.2 shows the basic theory of SAR or synthetic aperture processing including the
Doppler effect, matched filter and azimuth compression.
SAR continues to receive return pulses from a target during the time the radar projects the
beam to the target. In the meanwhile the relative distance between the radar and the target
changes with the movement of the platform, which produces a Doppler effect to modulate a
chirp modulation of received pulse. A matched filter corresponding to the reverse
characteristics of chirp modulation will increase the azimuth resolution of azimuth direction.
This is called azimuth compression.
In the case of SAR, unsuitability of satellite velocity and attitude will reduce the effect of the
Doppler effect. Therefore the satellite with SAR is required to be high, because the correction
for synthetic aperture processing due to instability at lower altitudes is very difficult.
Synthetic Aperture Radar (SAR) is a mode run by a conventional radar system that is moving (typically
on an airplane or satellite), which is typically used to obtain a highly detailed map of the ground. SAR
works by taking radar samples as the radar antenna is moving along a lineDue to the fact that SAR
maps use radio waves instead of light waves to create images, detailed SAR maps can be taken at
night or bad weather, when conventional optical or infrared cameras (EO/IR) cannot effectively
operate.
Basic Theory of SAR:
SAR is created on several extremely clever insights from physics and engineering. From antenna
theory, a larger aperture (think antenna size) creates a narrower antenna beamwidth, allowing for
greater antenna resolution and gain. Using antenna phased array theory, an antenna aperture can be
created or represented from multiple point sources. SAR exploits this physics by taking a single
moving antenna or small antenna array and using it to take a line of images mimicking multiple point
sources. Through careful signal processing techniques, the data from each radar reading as the
antenna moves in space can be used to gain much higher resolution of an object than with a single
reading or a stationary antenna. In the case of a flying airplane using SAR, an antenna less than a
meter in diameter can be used to effectively create a synthetic antenna aperture of over a thousand
meters in diameter.
Phased array antenna theory using multiple antennas (or point sources):
Aircraft radar takes measurements as aircraft moves in a line:
Synthetic aperture radar systems use beamforming to aim the signal in a direction that is
perpendicular to the path that the system is traveling in. So what does this mean? If one of
these systems is operating from a plane, for instance, it is aiming the beam sideways down
towards the surface. These radar systems use the movement of the radar antenna over the
target area to simulate the effect of a much larger array of antennas; this is what is meant by
the term “synthetic” aperture. This technology can create highly detailed two-dimensional and
three-dimensional images of landscapes.
In military applications, synthetic aperture radar is used to detect surface features like
building complexes and missile sites, as well as the topographical features of the terrain
surrounding them. This intelligence can be used in mission planning for future operations.
Synthetic aperture systems are also used in scientific applications. It has been used by NASA
to create high-resolution images for astronomical research. Because it can scan through clouds
very well, it has been used to penetrate through the dense atmosphere of Venus, and to
glimpse the mysterious hydrocarbon lakes on Saturn’s moon Titan, generating stunning,
never-before-seen views of the surfaces of these worlds.
Inverse Synthetic Aperture Radar operates under the same basic principle but with one key
difference: ISAR uses the movement of the target itself to generate its reading, rather than the
movement of the radar emitter. ISAR is used in military applications for identifying and
targeting objects by their movement. The US Navy uses ISAR in aircraft like the Boeing P8A to track ships and submarines.
Like conventional synthetic aperture radar, ISAR systems are also used in space-based
research to track moving objects. One notable example was when the Arecibo Observatory in
Puerto Rico used ISAR to generate highly detailed images of the asteroid 216 Kleopatra, a
dog bone-shaped asteroid roughly the size of New Jersey which orbits the sun between Mars
and Jupiter.
Whether it’s synthetic aperture radar, phased array, or any other category, all forms of radar
require high-quality, low-phase noise oscillator designs to generate the signals they need to
function. Radar tech is advancing at a rapid pace in the military and non-military domains,
and companies, governments, and researchers who want to make sure they’re getting the most
accurate readings need to use only the best components in their systems.
MIMO radar system is a novel radar method in which MIMO stands for Multiple Input
Multiple Output. It is a system of multiple antennas. Each transmit antenna radiates an
arbitrary waveform independently of the other transmitting antennas. Each receiving antenna
can receive these signals. Due to the different wave forms, the echo signals can be re-assigned
to the single transmitter. From an antenna field of N transmitters and a field of K receivers
mathematically results in a virtual field of K·N elements with in enlarged size of a virtual
aperture.
MIMO radar systems can be used to improve the spatial resolution, and they provide a
substantially improved immunity to interference. By improving the signal-to-noise ratio, the
probability of detection of the targets is also increased.
The MIMO radar systems can be classified into two categories:
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MIMO radar with colocated antennas (so called “Mono-Static” MIMO)
MIMO radar with widely separated antennas (so called “distributed” or “Bi-Static” MIMO).
The target is regarded by each antenna from another aspect angle.
“Mono-Static” MIMO
In the colocated radar case, the transmitting antennas are close enough such that the target
radar cross sections (RCS) observed by the transmitting antenna elements are identical. This
system is similar to a thinned array of a phased array antenna in which each radiator has its
own transceiver module and its own A/D converter. However, in a phased array antenna, each
radiator only transmits (possibly time-shifted) a copy of a transmission signal, which has been
generated in a central waveform generator. In a MIMO radar system each radiator has its own
arbitrary waveform generator and subsequently each radiator uses an individual waveform.
This individual waveform is also the basis for an assignment of the echo signals to their
source. If in publications are compared MIMO and phased array then the phased array
antenna is often depicted as SIMO (Single Input Multiple Output).
“Bi-Static” MIMO
In the distributed arrangement of the antennas, the radar data processing is much more
complex. In contrast to “Mono-Static” MIMO, each radar antenna looks at the target from a
different aspect angle. Therefore the target provides a different radar cross section for each
radar antenna. This requires much more complex target models for radar data processing.
What is MIMO Wireless Technology
MIMO: Multiple Input Multiple Output technology uses multiple antennas to make use of
reflected signals to provide gains in channel robustness and throughput.
Multiple-input multiple-output, or MIMO, is a radio communications technology or RF
technology that is being mentioned and used in many new technologies these days.
Wi-Fi, LTE; Long Term Evolution, and many other radio, wireless and RF technologies are
using the new MIMO wireless technology to provide increased link capacity and spectral
efficiency combined with improved link reliability using what were previously seen as
interference paths.
Even now many there are many MIMO wireless routers on the market, and as this RF
technology is becoming more widespread, more MIMO routers and other items of wireless
MIMO equipment will be seen.
MIMO -Multiple Input Multiple Output basics
A channel may be affected by fading and this will impact the signal to noise ratio. In turn this
will impact the error rate, assuming digital data is being transmitted. The principle of diversity
is to provide the receiver with multiple versions of the same signal. If these can be made to be
affected in different ways by the signal path, the probability that they will all be affected at
the same time is considerably reduced. Accordingly, diversity helps to stabilise a link and
improves performance, reducing error rate.
Several different diversity modes are available and provide a number of advantages:
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Time diversity: Using time diversity, a message may be transmitted at different times, e.g.
using different timeslots and channel coding.
Frequency diversity: This form of diversity uses different frequencies. It may be in the form
of using different channels, or technologies such as spread spectrum / OFDM.
Space diversity : Space diversity used in the broadest sense of the definition is used as the
basis for MIMO. It uses antennas located in different positions to take advantage of the
different radio paths that exist in a typical terrestrial environment.
MIMO is effectively a radio antenna technology as it uses multiple antennas at the transmitter
and receiver to enable a variety of signal paths to carry the data, choosing separate paths for
each antenna to enable multiple signal paths to be used.
General Outline of MIMO system
One of the core ideas behind MIMO wireless systems space-time signal processing in which
time (the natural dimension of digital communication data) is complemented with the spatial
dimension inherent in the use of multiple spatially distributed antennas, i.e. the use of multiple
antennas located at different points. Accordingly MIMO wireless systems can be viewed as a
logical extension to the smart antennas that have been used for many years to improve
wireless.
It is found between a transmitter and a receiver, the signal can take many paths. Additionally
by moving the antennas even a small distance the paths used will change. The variety of paths
available occurs as a result of the number of objects that appear to the side or even in the
direct path between the transmitter and receiver. Previously these multiple paths only served
to introduce interference. By using MIMO, these additional paths can be used to advantage.
They can be used to provide additional robustness to the radio link by improving the signal to
noise ratio, or by increasing the link data capacity.
The two main formats for MIMO are given below:
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Spatial diversity: Spatial diversity used in this narrower sense often refers to transmit and
receive diversity. These two methodologies are used to provide improvements in the signal
to noise ratio and they are characterised by improving the reliability of the system with
respect to the various forms of fading.
Spatial multiplexing : This form of MIMO is used to provide additional data capacity by
utilising the different paths to carry additional traffic, i.e. increasing the data throughput
capability.
As a result of the use multiple antennas, MIMO wireless technology is able to considerably
increase the capacity of a given channel while still obeying Shannon's law. By increasing the
number of receive and transmit antennas it is possible to linearly increase the throughput of
the channel with every pair of antennas added to the system. This makes MIMO wireless
technology one of the most important wireless techniques to be employed in recent years. As
spectral bandwidth is becoming an ever more valuable commodity for radio communications
systems, techniques are needed to use the available bandwidth more effectively. MIMO
wireless technology is one of these techniques.
MIMO Spatial Multiplexing
- overview of MIMO - Multiple Input Multiple Output, spatial multiplexing used to provide
One of the key advantages of MIMO spatial multiplexing is the fact that it is able to provide
additional data capacity. MIMO spatial multiplexing achieves this by utilising the multiple
paths and effectively using them as additional "channels" to carry data.
The maximum amount of data that can be carried by a radio channel is limited by the physical
boundaries defined under Shannon's Law.
Shannon's Law and MIMO spatial multiplexing
As with many areas of science, there a theoretical boundaries, beyond which it is not possible
to proceed. This is true for the amount of data that can be passed along a specific channel in
the presence of noise. The law that governs this is called Shannon's Law, named after the man
who formulated it. This is particularly important because MIMO wireless technology provides
a method not of breaking the law, but increasing data rates beyond those possible on a single
channel without its use.
Shannon's law defines the maximum rate at which error free data can be transmitted over a
given bandwidth in the presence of noise. It is usually expressed in the form:
C = W log2(1 + S/N )
Where C is the channel capacity in bits per second, W is the bandwidth in Hertz, and S/N is
the SNR (Signal to Noise Ratio).
From this it can be seen that there is an ultimate limit on the capacity of a channel with a
given bandwidth. However before this point is reached, the capacity is also limited by the
signal to noise ratio of the received signal.
In view of these limits many decisions need to be made about the way in which a transmission
is made. The modulation scheme can play a major part in this. The channel capacity can be
increased by using higher order modulation schemes, but these require a better signal to noise
ratio than the lower order modulation schemes. Thus a balance exists between the data rate
and the allowable error rate, signal to noise ratio and power that can be transmitted.
While some improvements can be made in terms of optimising the modulation scheme and
improving the signal to noise ratio, these improvements are not always easy or cheap and they
are invariably a compromise, balancing the various factors involved. It is therefore necessary
to look at other ways of improving the data throughput for individual channels. MIMO is one
way in which wireless communications can be improved and as a result it is receiving a
considerable degree of interest.
MIMO spatial multiplexing
To take advantage of the additional throughput capability, MIMO utilises several sets of
antennas. In many MIMO systems, just two are used, but there is no reason why further
antennas cannot be employed and this increases the throughput. In any case for MIMO spatial
multiplexing the number of receive antennas must be equal to or greater than the number of
transmit antennas.
To take advantage of the additional throughput offered, MIMO wireless systems utilise a
matrix mathematical approach. Data streams t1, t2, . . . tn can be transmitted from antennas 1,
2, . . . n. Then there are a variety of paths that can be used with each path having different
channel properties. To enable the receiver to be able to differentiate between the different data
streams it is necessary to use. These can be represented by the properties h12, travelling from
transmit antenna one to receive antenna 2 and so forth. In this way for a three transmit, three
receive antenna system a matrix can be set up:
r1 = h11 t1 + h21 t2 + h31 t3
r2 = h12 t1 + h22 t2 + h32 t3
r3 = h13 t1 + h23 t2 + h33 t3
Where r1 = signal received at antenna 1, r2 is the signal received at antenna 2 and so forth.
In matrix format this can be represented as:
[R] = [H] x [T]
To recover the transmitted data-stream at the receiver it is necessary to perform a considerable
amount of signal processing. First the MIMO system decoder must estimate the individual
channel transfer characteristic hij to determine the channel transfer matrix. Once all of this has
been estimated, then the matrix [H] has been produced and the transmitted data streams can
be reconstructed by multiplying the received vector with the inverse of the transfer matrix.
[T] = [H]-1 x [R]
This process can be likened to the solving of a set of N linear simultaneous equations to reveal
the values of N variables.
In reality the situation is a little more difficult than this as propagation is never quite this
straightforward, and in addition to this each variable consists of an ongoing data stream, this
nevertheless demonstrates the basic principle behind MIMO wireless systems.
MIMO Space Time Block Coding and Alamouti Codes
In order that MIMO spatial multiplexing can be utilised, it is necessary to add coding to the
different channels so that the receiver can detect the correct data.
There are various forms of terminology used including Space-Time Block Code - STBC,
MIMO precoding, MIMO coding, and Alamouti codes.
Space time block codes
Space-time block codes are used for MIMO systems to enable the transmission of multiple
copies of a data stream across a number of antennas and to exploit the various received
versions of the data to improve the reliability of data-transfer. Space-time coding combines all
the copies of the received signal in an optimal way to extract as much information from each
of them as possible.
Space time block coding uses both spatial and temporal diversity and in this way enables
Space-time coding involves the transmission of multiple copies of the data. This helps to
compensate for the channel problems such as fading and thermal noise. Although there is
redundancy in the data some copies may arrive less corrupted at the receiver.
When using space-time block coding, the data stream is encoded in blocks prior to
transmission. These data blocks are then distributed among the multiple antennas (which are
spaced apart to decorrelate the transmission paths) and the data is also spaced across time.
A space time block code is usually represented by a matrix. Each row represents a time slot
and each column represents one antenna's transmissions over time.
Within this matrix, Sij is the modulated symbol to be transmitted in time slot i from antenna j.
There are to be T time slots and nT transmit antennas as well as nR receive antennas. This
block is usually considered to be of 'length' T.
MIMO Alamouti coding
A particularly elegant scheme for MIMO coding was developed by Alamouti. The associated
codes are often called MIMO Alamouti codes or just Alamouti codes.
The MIMO Alamouti scheme is an ingenious transmit diversity scheme for two transmit
antennas that does not require transmit channel knowledge. The MIMO Alamouti code is a
simple space time block code that he developed in 1998.
Differential space time block code
Differential space time block coding is a form of space time block coding that does not need
to know the channel impairments in order for the signal to be decoded. The differential space
time block codes are normally based upon the more standard space-time block codes. One
block-code is transmitted from a set in response to a change in the input signal. This enables
the system to work because the differences among the blocks in the set are designed to allow
the receiver to extract the data with good reliability.
MIMO Antenna Beamforming
- overview of the basics of MIMO antenna technology including MIMO beamforming antenna
technology.
The MIMO antenna technologies used are key to the overall MIMO performance.
Additionally MIMO beamforming is an option that is coming to the fore.
As various forms of technology improve the MIMO antenna technology can be pushed further
allowing techniques like MIMO beamforming to be considered.
MIMO antenna & MIMO beamforming development
For many years antenna technology has been used to improve the performance of systems.
Directive antennas have been used for very many years to improve signal levels and reduce
interference.
Directive antenna systems have, for example, been used to improve the capacity of cellular
telecommunications systems. By splitting a cell site into sector where each antenna
illuminates 60° or 120° the capacity can be greatly increased - tripled when using 120°
antennas.
With the development of more adaptive systems and greater levels of processing power, it is
possible to utilise antenna beamforming techniques with systems such as MIMO.
MIMO beamforming smart antennas
Beamforming techniques can be used with any antenna system - not just on MIMO systems.
They are used to create a certain required antenna directive pattern to give the required
performance under the given conditions.
Smart antennas are normally used - these are antennas that can be controlled automatically
according the required performance and the prevailing conditions.
Smart antennas can be divided into two groups:
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Phased array systems: Phased array systems are switched and have a number of predefined patterns - the required one being switched according to the direction required.
Adaptive array systems (AAS): This type of antenna uses what is termed adaptive
beamforming and it has an infinite number of patterns and can be adjusted to the
requirements in real time.
MIMO beamforming using phased array systems requires the overall system to determine the
direction of arrival of the incoming signal and then switch in the most appropriate beam. This
is something of a compromise because the fixed beam is unlikely to exactly match the
required direction.
Adaptive array systems are able to direct the beam in the exact direction needed, and also
move the beam in real time - this is a particular advantage for moving systems - a factor that
often happens with mobile telecommunications. However the cost is the considerable extra
complexity required.
MU-MIMO Multi-User MIMO
- multi-user MIMO or MU-MIMO is form of advanced Multiple Input Multiple Output
technology used for providing multiple users access to a base station. It includes MIMO-BC
and MIMO-MAC.
Multi-user MIMO or MU-MIMO is an enhanced form of MIMO technology that is gaining
acceptance. MU-MIMO, Multi-user MIMO enables multiple independent radio terminals to
access a system enhancing the communication capabilities of each individual terminal.
Accordingly it is often considered as an extension of Space Division Multiple Access, SDMA.
MU-MIMO exploits the maximum system capacity by scheduling multiple users to be able to
simultaneously access the same channel using the spatial degrees of freedom offered by
MIMO.
To enable MU-MIMO to be used there are several approaches that can be adopted, and a
number of applications / versions that are available.
MU-MIMO vs SU-MIMO
Both Single User-MIMO and Multi-User MIMO systems can be sued within wireless and
cellular telecommunications systems. Each form of MIMO has its advantages and
Comparison of Mu-MIMO vs SU-MIMO
Feature
Main feature
Key aspect
MU-MIMO
For Mu-MIMO the base station is able
Base station communicates with a
to separately communicate with
single user.
multiple users.
Using MU-MIMO provides capacity
gain.
Provides increased data rate for the
single user.
Multiplexing gain.
Interference reduction
MU-MIMO provides a higher
Data throughput throughput when the signal to noise
ratio is high.
Channel State
Information
SU-MIMO
Perfect CSI is required.
Provides a higher throughput for a low
signal to noise ratio.
No CSI needed.
MU-MIMO basics
MU-MIMO provides a methodology whereby spatial sharing of channels can be achieved.
This can be achieved at the cost of additional hardware - filters and antennas - but the
incorporation does not come at the expense of additional bandwidth as is the case when
technologies such as FDMA, TDMA or CDMA are used.
When using spatial multiplexing, MU-MIMO, the interference between the different users on
the same channel is accommodated by the use of additional antennas, and additional
processing when enable the spatial separation of the different users.
There are two scenarios associated with MU-MIMO, Multi-user MIMO:
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Uplink - Multiple Access Channel, MAC: The development of the MIMO-MAC is based on
the known single user MIMO concepts broadened out to account for multiple users.
Downlink - Broadcast Channel, BC : The MIMO-BC is the more challenging scenario. The
optimum strategy involves pre-interference cancellation techniques known as "Dirty Paper
Coding", DPC - see below. This is complemented by implicit user scheduling and a power
MU-MIMO, Multi-user MIMO offers some significant advantages over other techniques:
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MU-MIMO systems enable a level of direct gain to be obtained in a multiple access capacity
arising from the multi-user multiplexing schemes. This is proportional to the number of base
station antennas employed.
MU-MIMO appears to be affected less by some propagation issues that affect single user
MIMO systems. These include channel rank loss and antenna correlation - although channel
correlation still affects diversity on a per user basis, it is not a major issue for multi-user
diversity.
MU-MIMO allows spatial multiplexing gain to be achieved at the base station without the
need for multiple antennas at the UE. This allows for the production of cheap remote
terminals - the intelligence and cost is included within the base station.
The advantages of using multi-user MIMO, MU-MIMO come at a cost of additional hardware
- antennas and processing - and also obtaining the channel state information which requires
the use of the available bandwidth.
MIMO-MAC
This form of MU-MIMO is used for a multiple access channel - hence MIMO and it is used in
For the MIMO-MAC the receiver performs much of the processing - here the receiver needs
to know the channel state and uses Channel Sate Information at the Receiver, CSIR.
Determining CSIR is generally easier than determining CSIT, but it requires significant levels
of uplink capacity to transmit the dedicated pilots from each user. However MIMO MAC
systems outperform point-to-point MIMO particularly if the number of receiver antennas is
greater than the number of transmit antennas at each user.
MIMO-BC
This form of MU-MIMO is used for the MIMO broadcast channels, i.e. the downlink. Of the
two channels, BC and MAC, it is the broadcast channel that is the more challenging within
MU-MIMO.
Transmit processing is required for this and it is typically in the form of pre-coding and
SDMA, Space Division Multiple Access based downlink user scheduling. For this the
transmitter has to know the Channel State Information at the Transmitter, CSIT. This enables
significant throughput improvements over that of ordinary point to point MIMO systems,
especially when the number of transmit antennas exceeds that of the antennas at each receiver.
Dirty Paper Coding, DPC
Dirty Paper Coding, DPC is a technique used within telecommunications scenarios,
particularly wireless communications to provide efficient transmission of digital data through
a channel that is subject to interference, the nature of which is known to the transmitter.
The Dirty Paper Coding, DPC, technique consists of precoding the data so the interference
data can be read in the presence of the interference. The pre-coding normally uses the
Channel State Information.
To explain Dirty Paper Coding, DPC, an analogy of writing on dirty paper can be used.
Normally black ink would be used, but if the paper is dirty, i.e. black, then the writing cannot
be read. However if the writing was in white, although it could not be read on white paper, it
would be perfectly legible on black, or dirty paper. The same technique is used on the data
transmission, although the nature of the interference must be known so that the pre-coding can
be incorporated to counter the effect of the interference.
Multi-user MIMO is still in its infancy, and many developments are underway to determine
the optimum formats for its use. Coding types as well as levels of channel state indication are
being determined as these use up valuable resource and can detract from the overall data
throughput available. However the significant gains that can be made by using MU-MIMO,
multi-user MIMO mean that it will be introduced in the foreseeable future
Massive MIMO, Large MIMO Systems
Massive MIMO or large MIMO systems technology is being developed for use in many
Large MIMO systems, often referred to as massive MIMO systems, can be defined as those
that use tens or hundreds of antennas in the communication terminals.
Traditional MIMO systems may have two or four, some may even have eight antennas, but
this has been the limit on early systems that have adopted MIMO.
The concept of massive MIMO or large MIMO systems is entering many areas of
development as it is able to offer some distinct advantages.
Massive MIMO benefits
There are many advantages to using large MIMO technology. Using more antennas in a
MIMO system creates more degrees of freedom in the spatial domain and therefore this
enables greater improvement in performance to be achieved:
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Increasing data rate: The increase in the number of antennas allows for a greater number
of paths to be used and hence a much greater level of data to be transferred within a given
time.
Increasing basic link signal to noise ratio: One of the basic advantages of the use of MIMO
systems is that it can be used to improve the signal to noise ratio of the overall system. The
use of large MIMO or massive MIMO enables this to be taken to a greater level. There is also
an increase in hardening against intentional jamming as a result of the large diversity.
Channel hardening: Increasing the number of antennas significantly to make a massive
MIMO system means that the system becomes less sensitive to the actual entries of the
channel matrix. In turn this has further advantages in the area of signal processing. It is
necessary for linear detectors to perform matrix inversions and this can be done more easily
within the processing as this capability increases with technology developments.
Antenna placement
One of the key issues with any MIMO system is the placement of the antennas. For many
systems using physically small units, the antenna placement can present some issues.
In order that the MIMO system is able to operate satisfactorily, the correlation between
antennas must be small. As a rule of thumb, s spacing of λ/2 (where λ is the wavelength of the
signal) is considered necessary to provide almost no correlation between the antennas.
In order to achieve this a variety of approaches can be taken.
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Use high frequencies: In order to be able to accommodate the higher antenna numbers
required for large MIMO systems, the use of higher frequencies shortens the wavelength of
the signals, thereby allowing the antenna spacing in terms of wavelengths to be
accommodated within a given physical space. Many systems are considering the use of
frequencies above 10 GHz, extending even as far as 60GHz and beyond and .
Use volumetric rather than linear spacing: It is possible to use the three dimensions within
an item to provide spacing within three dimensions rather than just two dimensions as in a
linear fashion. Although many items, including mobile phones are often thin and therefore
this approach may not be applicable, in some instances a cube will be able to accommodate
more antennas by using spacing in three dimensions.
Use of spatial modulation: The number of RF chains needed for a massive MIMO system
can be reduced without compromising the spectral efficiency by using spatial modulation.
Spatial modulation is a form of modulation that only requires the use of one transit chain for
multiple antennas. Effectively it uses one antenna from an array at a time for transmission.
Spatial modulation adopts a simple but effective coding mechanism which establishes a one
to one mapping between blocks of transmitted information bits and the spatial positions of
the transmitter antenna in the overall antenna array.
Massive MIMO paths
One of the key requirements for a large MIMO system is that there is a rich diversity of signal
paths between the transmitter and receiver. This is normally present within a typical indoor
and most urban environments. Other environments where there are less paths will not be able
to provide the same benefits with a MIMO let alone a large MIMO system as fewer paths will
be available.
Inadequate spacing between the antennas will mean that they tend to correlate more and the
gains of MIMO systems cannot be realised.
Another situation that can occur, even in a rich scattering environment is where all the paths
pass through a pinch point. This can result in there being fewer independent spatial
dimensions and accordingly the performance will be reduced.
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