Comparitive analysis of smart antenna array basis of beamforming schemes and algorithems A Review - PDF

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Comparitive analysis of smart antenna array basis of beamforming schemes and algorithems A Review - PDF Powered By Docstoc
					                                                        (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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                                                                                                    ISSN 1947-5500
                                                                     (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


                                                                                 124                               http://sites.google.com/site/ijcsis/
                                                                                                                   ISSN 1947-5500
                                                        (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


                                                                 125                                 http://sites.google.com/site/ijcsis/
                                                                                                     ISSN 1947-5500
                                                        (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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                                                                                                   ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 8, No. 2, May 2010




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.
are deep nulls in the directions of the interference signals,
which can be seen from the directional pattern of the antenna,                                    REFERENCES
Through these constrained conditions, the interference signals       [1]   Fang-Biau Ueng, Jun-Da Chen and Sheng-Han Cheng “Smart Antennas
can be suppressed and the output power of the array can be                 for Multi-user DS/CDMA communications in Multipath Fading
minimized to suppress other signals and noises which are not               Channels” IEEE Eighth international symposium on spread spectrum
                                                                           ISSSTA2004, Sydney, Australia, 30 Aug. - 2 Sep. 2004
located in the main lobe of the antenna. The weight vector of
the LCMV algorithm is:                                               [2]   Alexiou, A. , Haardt, M. “Smart antenna technologies for future
                                                                           wireless systems: trends and challenges “ IEEE Communications
                                                                           Magazine,Volume 42, Issue 9, Page(s):90 - 97 Sept. 2004
                                                                     [3]   A. Rawat “Smart antenna terminal development” National conf. of
                               (7)                                         IETE Chandigadh, India April 2005
    From above equation, we can see that in DS-CDMA                  [4]    A. Rawat ”Design of smart antenna system for military application
systems, the above two algorithms, namely SMI and LCMV                     using mat lab” National conf. of Institution of Engineers in Jaipur ,
                                                                           India Aug 2006.
algorithms, can be used by the adaptive antenna array for
propagation delay estimation. The large sample maximum               [5]   Chryssomallis, M.” smarty antennas” Antennas and Propagation
                                                                           Magazine, IEEE Volume 42, Issue 3, Page(s):129 – 136 June 2000
likelihood (LSML) is applied to the beam forming output data
                                                                     [6]   A. Paulraj, R. Nabar, and D. Gore, “Introduction to Space-Time
for estimating to the propagation delay of a desired user in               Wireless Communications”, Cambridge Univ. Press, 2003.
multi-user sceneries. The adaptive antenna array can help the        [7]   L. C. Codara, “Application of Antcnna Arrays to Mobile
LSML estimator to obtain improved performances as                          Communications, Part 11: Beam-Forming and Direclion-of-Arrival
compared to a single antenna based LSML estimator.                         Considerations,” Proceedings of the IEEE, 85, pp.1195-1245, 8,
                                                                           August 1997
E.    CMA Algorithm                                                  [8]   Lal C. Godra, Application of Antenna Array to Mobile
    In order to adaptively control directions of nulls, some               Communications, Part U : Beam-Forming and Direction-of-Arrival
information concerning incident waves such as directions and               Considerations”. Proceedings of the IEEE, Vol. 85, No. 8, Page(s):
                                                                           1213-1218, 1997.
intensity of incident waves is required. It is , however, very
                                                                     [9]   Jian-Wu Zhang “The Adaptive Algorithms of the Smart Antenna
difficult to know the information in some environment. In                  System in Future Mobile Telecommunication Systems” IEEE
addition, the directions and intensity may vary with the                   International Workshop on Antenna Technology pp347-350, 2005
variation of the environment. Thus the algorithm for
controlling the nulls is important especially in the case of

                                                                  127                                 http://sites.google.com/site/ijcsis/
                                                                                                      ISSN 1947-5500
                                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                                 Vol. 8, No. 2, May 2010




[10] Blair D. Carlson, “Covariance Matrix Estimation Erron and Diagonat       [26] R. Kohno, C. Yim and H. Imai “Array Antenna Beamforming Based on
     Loading in Adaptive Arrays”. IEEE Transactions on Aerospace and               Estimation on Arrival Angles Using DFT on Spatial Domain,”
     Electronic System. Vol. 24, No. 4, Page(s): 397-401, July 1988.               Proceedings of PIMRC 1991, London, UK, ,pp. 38-43 September 1991.
[11] Werner, S.; Apolinario, J.A.., Jr.; Lakkso, T.I. “Multiple-antenna       [27] Jumarie, G. “Nonlinear filtering: A weighted mean squares approach
     CDMA Mobile Reception Using Constrained Normalized Adaptive                   and a Bayesian one via the maximum entropy principle.”Signal
     Algorithms”, Telecommunications Symposium, 199s. ITS ’98                      Processing, 21 (1990), 323—338, 1990.
     Proceedings. SBT/IEEE International, Vol: 1, Page(s): 353-358 , 1998 .   [28] Sun, Q., Alouani, A. T., Rice, T. R., and Gray, J. E. “ Linear system
[12] Fujimoto, M.; Nishikawa, K.; Sato, K., “A Study of Adaptive Array             state estimation: A neurocomputing approach.” In Proceedings of the
     Antenna System for Land Mobile Communications”, Intelligent                   American Control Conference, 550—554, 1992.
     Vehicles’95 Symposium, Proceedings of the IEEE, Page(s): 36-41, 25-      [29] Cohen, S. A. “Adaptive variable update rate algorithm for tracking
     26 Sept, 1995.                                                                targets with a phased array radar’. IEE Proceedings, pt. F, 133, 277—
[13] Demo, S.; Ohira, T., “M-CMA for Digital Signal Processing Adaptive            280, 1986 .
     Antennas with Microwave Beamforming”,Proceedings of IEEE, Vol. 5,        [30] J.C. Liberti, T.S. Rappaport, “Smart Antennas forWireless
     Page(s): 179-187 ,2000 .                                                      Communications:        IS-95     and      Third-Generation      CDMA
[14] Hefnawi, M.; Delisle, G.Y. “Adaptive arrays for wideband interference         Applications”,Prentice Hall, NJ, 1999.
     suppression in wireless communications”, Antennas and Propagation        [31] LAL C. GODAR4, Application of Antenna Array to Mobile
     Society, 1999. IEEE International Symposium 1999, vok3, Page(s):              Communications, Part U : Beam-Forming and Direction-of-Arrival
     1588 - 1591, 1999.                                                            Considerations”. Proceedings of the IEEE, Vol. 85, No. 8, Page(s):
[15] Weijun Yao, and Yuanxun Ethan Wang, ”Beamforming for Phased                   1213-1218, 1997.
     Arrays on Vibrating Apertures”, IEEE Trans. Antennas Propag., vol.       [32] Sandgchoon Kim; Miller, S.L. “An Adaptive Antenna array Based
     54,no.10, Oct. 2006                                                           Propagation Delay Estimation for DS-CDMA Communication
[16] A. H. El Zooghby, C. G. Christodoulou, and M. Georgiopoulos “Neural           Systems”, Military Communications Conference, 1998. Milcom 98,
     Network-Based Adaptive Beamforming for One- and Two-                          Proceedings of the IEEE Vol: 1, Page(s):333-337, 1998 .
     Dimensional Antenna Arrays” IEEE Trans. Antennas Propag., vol. 46,       [33] Sandgchoon Kim; Miller, S.L. “An Adaptive Antenna array Based
     no. 12 pp1891 -1893, Dec. 1998.                                               Propagation Delay Estimation for DS-CDMA Communication
[17] Hugh L. Southall,Jeffrey A. Simmers, and Teresa H. O’Donnell                  Systems”, Military Communications Conference, 1998. Milcom 98,
     “Direction Finding in Phased Arrays with a Neural Network                     Proceedings of the IEEE Vol: 1, Page(s):333-337, 1998 .
     Beamformer” IEEE Trans. Antennas Propag., vol. 43,no. 12 pp 1369-        [34] BLAIR D. CARLSON, “Covariance Matrix Estimation Erron and
     1374 , Dec1995.                                                               Diagonat Loading in Adaptive Arrays”. IEEE Transactions on
[18] Robert J. Mailloux “Phased array antenna handbook” Artech                     Aerospace and Electronic System. Vol. 24, No. 4, Page(s): 397-401,
     House,2006 .                                                                  July 1988.
[19] Eric Charpentier, and Jean-Jacques Laurin, “An Implementation of a       [35] Ronald L. Dilsavor, Randolph L. Moses, “Analysis of Modified SMI
     Direction-Finding Antenna for Mobile Communications Using a Neural            method for adaptive Array Weight Control’, IEEE Transactions on
     Network” IEEE Trans. Antennas Propag., vol. 47, NO. 7pp 1152 -1158            Signal Processing, Vol. 41, No. 2, Page(s): 721-726,1993,.
     , JULY 1999.                                                             [36] Werner, S.; Apolinario, J.A.., Jr.; Lakkso, T.I. “Multiple-antenna
[20] B. K. Yeo and Y. Lu, “Array failure correction with a genetic                 CDMA Mobile Reception Using Constrained Normalized Adaptive
     algorithm,”IEEE Trans. Antennas Propag., vol. 47, no. 5, pp. 823–             Algorithms”, Telecommunications Symposium, 199s. ITS ’98
     828,1999.                                                                     Proceedings. SBT/IEEE International, Vol: 1, Page(s): 353-358 , 1998
[21] M. Salazar-Palma, T. K. Sarkar, L.-E. G. Castillo, T. Roy, and A.        [37] Fujimoto, M.; Nishikawa, K.; Sato, K., “A Study of Adaptive Array
     Djordjevic , Iterative and Self-        Adaptive Finite-Elements in           Antenna System for Land Mobile Communications”, Intelligent
     Electromagnetic Modeling. Norwood, MA: Artech House, 1998.                    Vehicles’95 Symposium, Proceedings of the IEEE, Page(s): 36-41, 25-
[22] Amalendu Patnaik, B. Choudhury, P. Pradhan, R. K. Mishra, and                 26 Sept, 1995.
     Christos Christodoulou “An ANN Application for Fault Finding in          [38] Demo, S.; Ohira Demo, S.; Ohira, T., “M-CMA for Digital Signal
     Antenna Arrays “ IEEE Trans. Antennas Propag., vol. 55, no.3pp 775-           Processing        Adaptive       Antennas        with        Microwave
     777, Mar. 2007.                                                               Beamforming”,Proceedings of IEEE, Vol. 5, Page(s): 179-187 ,2000 .
[23] R. F. Harrington, “Field Computation by Moment Methods”. New             [39] Hefnawi, M.; Delisle, G.Y. “Adaptive arrays for wideband interference
     York:IEEE Press, 1993.                                                        suppression in wireless communications”, Antennas and Propagation
[24] L. C. Codara, “Application of Antcnna Arrays to Mobile                        Society, 1999. IEEE International Symposium 1999, vok3, Page(s):
     Communications, Part 11: Beam-Forming and Direclion-of-Arrival                1588 - 1591, 1999.
     Considerations,” Proceedings of the IEEE, 85, 8, pp. 1195-1245,
     August 1997
[25] M. Nagatsuka, N. Ishii, R. Kohno and H. Imai, “Adaptive Array
     Antcnna Based on Spatial Spcctral Estimation Using Maximum
     Enlrapy Method,” IEICE Trnnsactioris on Corn,rru,ricutiorrs,E77-B, 5,
     pp. 624-633, 1994.




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DOCUMENT INFO
Description: 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.