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The smart antenna array is a group of antennas in which the relative phases of the respective signals feeding the antennas are varied in such a way that the effective radiation pattern of the array is reinforced in a desired direction and suppressed in undesired directions. Smart antenna are the array with smart signal processing algorithms used to identify spatial signal signature such as the direction of arriving of the signal, and use it to calculate beam forming vector, to track and locate the antenna beam on the mobile/target. An array antenna may be used to point a fixed radiation pattern, or to scan rapidly in azimuth or elevation. This paper explains the architecture; evolution of smart antenna differs from the basic format of antenna. The paper further discusses different Beamforming schemes and algorithms of smart antenna array.
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
Comparitive Analysis of Beamforming Schemes And
Algorithems of Smart Antenna Array : A Review
Abhishek Rawat , R. N. Yadav and S. C. Shrivastava
Maulana Azad National Institute Of Technology
Bhopal, INDIA
Abstract— The smart antenna array is a group of antennas in
which the relative phases of the respective signals feeding the
antennas are varied in such a way that the effective radiation
pattern of the array is reinforced in a desired direction and
suppressed in undesired directions. Smart antenna are the array
with smart signal processing algorithms used to identify spatial
signal signature such as the direction of arriving of the signal,
and use it to calculate beam forming vector, to track and locate
the antenna beam on the mobile/target. An array antenna may
be used to point a fixed radiation pattern, or to scan rapidly in
azimuth or elevation. This paper explains the architecture;
evolution of smart antenna differs from the basic format of
antenna. The paper further discusses different Beamforming
schemes and algorithms of smart antenna array.
I. INTRODUCTION
In the past, wireless communication systems are deployed
with fixed antenna system with fixed beam pattern. Such
configuration can not meet all the requirements of modern
communication environments. Smart antennas [1]-[2] are the Fig. 1. Principle of smart antenna.
technology that use a fix set of antenna elements in an array.
The signals from these antenna elements are combined to form characteristics (such as a known alphabet or constant
a movable beam pattern that can be steered to the direction of envelope) that the transmitted signal is known to have. The
the desired user. This characteristic makes the smart antenna base station antennas have up till now been omni directional
and minimizes the impact of noise, interference, and other or sectored. This can be regarded as a "waste" of power as
effects that degrade the signal quality. The adoption of smart most of it will be radiated in other directions than toward the
antenna techniques in future wireless systems is expected to user and the other users will experience the power radiated in
have a significant impact on the efficient use of the spectrum, other directions as interference [4]. The idea of smart
the minimization of the cost of establishing new wireless antennas is to use base station antenna patterns that are not
networks, the optimization of service quality, and realization fixed, but adapt to the current radio conditions. This can be
of transparent operation across multi technology wireless visualized as the antenna directing a beam toward the
networks [2]-[5]. Smart antenna systems consist of multiple communication partner only.
antenna elements at the transmitting and/or receiving side of
the communication link, whose signals are processed
adaptively in order to exploit the spatial dimension of the II. TYPES AND GEOMETRY OF SMART ANTENNA
mobile radio channel as shown in Fig.1. A smart antenna SYSTEMS
receiver can decode the data from a smart antenna transmitter Smart antenna systems can improve link quality by
this is the highest-performing configuration or it can simply combating the effects of multi-path propagation or
provide array gain or diversity gain to the desired signals constructively exploiting the different paths, and increase
transmitted from conventional transmitters and suppress the capacity by mitigating interference and allowing transmission
interference. No manual placement of antennas is required. of different data streams from different antennas [6]. Smart
The smart antenna electronically adapts to the environment by antenna system technologies include intelligent antennas,
looking for pilot tones or beacons or by recovering certain
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
Fig.2 Different array geometries for smart antennas Fig.3. Comparison between three basic types of smart antenna.
a) Uniform linear array b) Circular array;
c) 2-Dimensional grid array d) 3-Dimensional grid array
the system continuously updates beam selection, ensuring
TABLE I. COMPARISON BETWEEN THREE BASIC TYPE OF SMART that user gets optimal quality for their call. The system scans
ANTENNA. the outputs of each beam and selects the beam with the largest
S. Switched Lobe Dynamically Adaptive
output power as well as suppresses interference arriving from
No Phased Array Array directions away from the active beam’s center.[70]
1. A finite number It has fixed An infinite number
The dynamically phased array smart antenna is an antenna
of fixed, predefined number of array of patterns which controls its own pattern by means of feed-back or feed-
patterns or which can be (scenario-based) that forward control, and it performs gain enhancement for desired
combining strategies electronically are adjusted in real signals whereas suppression for interfering signals The phased
(sectors) steered in a time.
particular
array antenna consists of multiple stationary antenna
direction. elements, which are fed coherently and use variable phase or
time delay control at each element to scan a beam to given
2. This kind of Easy to Complex in
antenna will be move nature at the time
angle in space. Array can be used in place of fix aperture
easier to implement electronically. In of installment and antennas(reflectors , lenses ), because the multiplicity of
in existing cell this case, the best performance elements allows more precise control of radiation pattern, thus
structures than the received power is in the three types resulting in lower side band and careful pattern shaping .
more sophisticated maximized. of smart antennas.
adaptive arrays, The adaptive array system required sophisticated signal
which also means processing algorithm to distinguish between desired signal ,
low cost.
multipath signal and interference signal. It combine adaptive
3. The signal It does not Excellent digital signal processing to the spatial signal processing to
strength can degrade null the performance in achieve greater performance.
rapidly during the interference. interference.
beam switching.
III. BEAMFORMING SCHEMES OF SMART ANTENNA
ARRAY
phased array, digital beam forming, adaptive antenna
systems, and others. Smart antenna systems are customarily The Beamforming scheme is important factor to
categorized, however, as switched beam, dynamically phased convert antenna array into smart antenna. These schemes tilt
array and adaptive array systems [5].Switched lobe creates a the radiation pattern into desired direction depending upon
group of overlapping beams that together result in omni conditions. The simplest beamformer has all the weights of
directional coverage. The overlapping beam patterns pointing equal magnitudes, and is called a conventional Beamformer
in slightly different directions. The SBSA creates a number of or a delay-and sum beamformer. This array has unity
two-way spatial channels on a single conventional channel in response in the look direction, which means that the mean
frequency, time, or code. Each of these spatial channels has output power of the processor, due to a source in the look
the interference rejection capabilities of the array, depending
direction, is the same as the source power to steer the array in
on side lobe level [70].As the mobile moves, beam-switching
algorithms for each call determine when a particular beam a particular direction, the phases are selected appropriately.
should be selected to maintain the highest quality signal and This beamformer provides the maximum output SNR for the
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
case that no directional jammer operating at the same utilized by the constraint in the look direction. This may not
frequency exists, but it is not effective in the presence of he true in a mobile-communications environment with multi-
directional jammers, intentional or unintentional. Generally path arrivals, and the array Beamformer may not be able to
null steering and optimal Beamformer are the commonly achieve the maximization of the output SNR by suppressing
used in Smart antenna array . every interference. However, the Beamformer does not have
to fully suppress interference, since an increase of a few
A. Null-Steering Beamformer
Null-steering beamforming techniques require not only
control of phase (as for conventional beamforming), but also
independent control of the amplitude. A null-steering Beam
former can cancel a plane wave arriving from a known
direction, producing a null in the response pattern in this
direction. The process works well for canceling strong
interference, and could he repeated for multiple-interference
cancellation. But although it is easy to implement for signal
interference, it becomes cumbersome as the number of
interference grows. Although the beam pattern produce by this
Beamformer has nulls in the directions of interference [5], it is
not designed to minimize the uncorrelated noise at the array
output. This can be achieved by selecting weights that
minimize the mean output power, subject to the above
constraints. The flexibility of array weighting to being
adjusted to specify the array pattern is an important property.
This may be exploited to cancel directional sources operating
at the same frequency as that of the desired source, provided
these are not in the direction of the desired source. In
situations where the directions of these interferences are
known, cancellation is possible by placing the nulls in the Fig 4 The structure of a narrow band beam-former[10] (a)without
pattern corresponding to these directions and simultaneously reference signal.and (b) using a reference signal.
steering the main beam in the direction of the desired signal.
Beam forming in this way, where nulls are placed in the decibels in the output SNR can make a large increase in
directions of interferences, is normally known as null beam the channel capacity. In the optimization using reference
forming or null steering. The cancellation of one interference signal method, the processor requires a reference signal
by placing a null in the pattern uses one degree of the freedom instead of the desired signal direction (Fig.4). The array output
of the array. Null beam forming uses the directions of sources is subtracted from an available reference signal to generate an
toward which nulls are placed for estimating the required error signal, which is used to control the weights. Weights are
weighting on each element. There are other schemes that do adjusted such that the mean squared error (MSE) between the
not require directions of all sources. A constrained array output and the reference signal is minimized. Arrays
Beamforming scheme uses the steering vector associated with which use zero reference signals are referred to as power-
the desired signal and then estimates the weights by solving an inversion adaptive arrays. The MSE minimization sachem is a
optimization problem. Knowledge of the steering vector closed-loop method, compared to the open –loop scheme of
associated with the desired signal is required to protect the MVDR (the ML filter), and the increased SNR is achieved at
signal from being canceled. In situations where the steering the cost of some signal distortion, caused by the filter.
vector associated with the signal is not available, a reference
signal is used for this purpose [54].
IV. GENERALLY USED SMART ANTENNA
B. Optimal Beamformer ALGORITHMS
The optimal Beamformer referred also as the optimal
combiner or minimum variance distortion less response beam At present, there are many sorts of algorithms that
former (MVDR), does not require knowledge of the direction can be applied to the smart antenna systems. People also put
and the power level of interference ,nor the level of the forward many modified algorithms on the basis of the basic
background noise power , to maximize the output SNR. In this algorithms to adapt to different performance demands.
case the weights are computed assuming all source as Generally, there are two categories: blind algorithm and non
interference and processor is referred to as a noise along blind algorithm. The algorithm that needs the reference signal
matrix inverse(NAMI) or maximum likelihood (ML) filter ,as to adjust the weights gradually is referred to as the blind
it finds the ML estimate of the power of the signal source with algorithm. Besides, when the directions of the signals are
the above assumption. Minimizing the total output noise, known, we can determine the channel response firstly, and
while keeping the output signal constant, is the same as then determine the weights according to certain principle.
maximizing the output SNR. This method requires the number This kind of algorithms includes LMS, RLS, SMI, LCMV
of interferers to be less than or equal to L -2, as an array with and so on. Inversely, the blind algorithm doesn’t need the
L elements has L- 1 degrees of freedom, and one has been reference signal. The receiver can estimate the transmitted
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
signal and treat it as the reference signal to make signal
processing. This kind of algorithm makes use of the inherent
characteristics of the modulating signal or the characteristics
that is independent of the carried information. This kind of (2)
algorithms includes CMA, subspace algorithm, MUSIC
algorithm, and so on. Moreover, the two kind of algorithm Where the inverse matrix is updated as
can also be combined, namely, using the non blind algorithm
to determine the initial value and then using the blind
algorithm to track and adjust, such as SMI+CMA[l]. This
method is suitable to the communication system that
transmits the pilot symbols. (3)
A. LMS Algorithm
Where
The LMS algorithm is based on the principle of the
steepest descend and is applied to the MSE performance C. Sample Matrix Inversion (SMI) Algorithm
measurement. The LMS algorithm intrudes three categories The SMI algorithm estimates the weights directly by
[52] unconstrained LMS algorithm, normalized LMS estimating the covariance matrix R from K independent
algorithm and constrained LMS algorithm. When the weights samples of data by time- averaging. Thus the problem that the
are subjected to constraints at each iteration, the algorithm is rate of the convergence depends on the eigen value
referred to as the constrained LMS algorithm. Otherwise, it is distribution can be avoided. The optimum solution obtained
referred to as an unconstrained LMS algorithm. The from the SMI algorithm is[55 ].
unconstrained LMS algorithm is mostly applicable when
weights are updated using a reference signal and no -1
knowledge of the direction of the signal is utilized. Though (4)
the structure of the normal LMS algorithms are very simple, it
doesn’t perform well due to its slow convergence rate in
situation of fast-changing signal characteristics and the high H
sensitivity to the eigen value distribution of the covariance Where
matrix of the array signals, which limits its application in
CDMA system. The normalized LMS algorithm is a variation i is a complex sample vector of receiver outputs of
of the constant-step-size LMS algorithm, and uses a data- length N, N is the number of elements of the array antenna, K
dependent step size at each iteration . is the number of sample vectors used. V is a steering vector of
length N which is equal to the un adapted array weights.
μ Forming a sample covariance matrix and solving for the
μ ( n) = weights provides a very fast rate of convergence. The rate of
( n ) X ( n)
H
X convergence is dependent only on the number of elements and
(1) is independent of the noise and interference environment and
The algorithm normally has better convergence the eigen value distribution. Because the complexity of the
performance and less signal sensitivity compared to the computing is proportional to N3 so it requires that the
normal LMS algorithm. When applied to the multi-antenna algorithm has a strong processing ability when the array is
CDMA mobile systems, using an optimal step-sequence in the large. To a certain given value of K, the quality of the
update, the algorithm can achieve a fast convergence and a estimation obtained from the time average is dependent on the
near-optimum steady-state performance at the expense of low input signal-noise ratio (SNR). When the SNR decreases, in
increase in the complexity than the normal LMS order to eliminate noise and interference, a large amount of
algorithm[53]. Moreover, a modified and normalized. LMS samples are needed to obtain the estimation more precisely .
(MN-LMS) algorithm is presented in [43]. The adaptive filter Ronald L. etc had ever put forward the M-SMI algorithm[66],
using this algorithm can track the individual total input phase namely the modified SMI, in which the diagonal loading
at each element and the channel estimation and phase technique is used, where, the diagonal of the covariance
calibration are not required for the inverse link improvement. matrix is augmented with a positive or negative constant prior
to inversion. Compared to the SM1 algorithm, the diagonally
B. RLS Algorithm loaded sample covariance matrix
The RLS algorithm is based on the LS rule to make the
error square-sum of the array output in each snapshot least . = (5)
This algorithm take advantage of all the array data information
that obtained after the initiation of the algorithm and using the F can be positive or negative, but for the covariance
iteration method to realize the inverse operation of the matrix, matrix to be positive definite. The positive loading tends to
so the convergence rate is rapid and can realize the tradeoff reduce the null depth on weak interfering signal, while it
between the rate of the convergence and the computing decreases the convergence time. Conversely, negative loading
complexity. This algorithm is not sensitive to the eigen value tends to increase the null depth on weak interfering signals
distribution, but compared to the normal LMS algorithm, its while increasing convergence time. The SMI algorithm can
computational complexity is high[54].The common solution get the maximum signal-to-interference-plus-noise (SINR).
of the algorithm is However, in some applications, such as digital
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communications or satellite television communications, other above environment. The CMA algorithm can solve the
measures of performance such as SIR may be equally problem [58]. It is a typical blind algorithm and only requires
important, the M-SMI can be applied in this situation. that the amplitude of the transmitted signal is constant, such as
FM, PSK, FSK etc. CMA is based on the fact that the
D. LCMV Algorithm amplitude of the combined signal fluctuates because of the
The algorithms mentioned above all need the reference interference. Thus, in CMA. the amplitude of the combined
signal, and the reference signal must have Large correlation signal is always observed, and the weights coefficients are
with the desired signal. But in actual environment, this is adjusted so as to minimize the variation of the amplitude of
difficult to obtain. So we can make use of the technology of the signal. When the output amplitude becomes constant, nulls
orientation of the reference signal source. In the environment can be formed in the direction of the interference signals on
that the signals are dense, we can orient the desired signal and the directional pattern. Moreover, Satoshi Denno etc have put
the interference signal sources, and then combine this with the forward the Modified CMA algorithm in [59].The use of
technology of nulling adaptively, thus we can obtain reference adaptive array to reject wideband interferences and track
nulling with high resolution. It is assumed that there are p wideband signals has been proven to be more efficient if
desired signals and q interference signals incident on the frequency compensation is used. Among the frequency
antenna. The directions of the incident signals are ( θi ….., θp ) compensation algorithms, the interpolating techniques have
and ( θp+1 …….., θp+q ) respectively in which p + q < M . The been applied to the CMA. ICMA permits to improve system
constrained condition of the LCMV algorithm[57] is: performances by readjusting the main lobe's direction toward
the signal's DOA and increasing the interference null depth
[60].
V. FURTHER REMARKES
(6) In this paper, we have discussed various Smart
Where antenna array architectures, Beamforming techniques and
algorithms. The design and architecture of smart antenna is
case sensitive and changed according to the demand of
applications. The adaptive array provide excellent result in
the presence of interference, but its design is more complex
and costly as compared of other two. In Beamforming null
steering Beamforming perform well in case of strong
This algorithm can ensure that the antenna has the gain of interferences, but in need prior information of that. The blind
1 in the directions of the desired signals, while the responses algorithm doesn’t need the reference signal so we can apply
in the directions of the interference signals are zero, thus there them according the communication system demands.
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